diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning | Paper Citation Record

Observation c16701d6-9e29-456b-a76a-ec9da3f0d088 · outbound

This paper cites Smoothed particle hydrodynamics: theory and application to non-spherical stars.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Smoothed particle hydrodynamics: theory and application to non-spherical stars

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.614901Z

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Observation cea6c571-d083-485f-ab88-3546330de404 · outbound

This paper cites Smoothed particle hydrodynamics in astrophysics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Smoothed particle hydrodynamics in astrophysics

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.622243Z

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Observation 798ec7a9-9fc3-4cba-8c6d-9efb54f5d9f9 · outbound

This paper cites Smoothed particle hydrodynamics (sph) for complex fluid flows: Recent developments in methodology and applications.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Smoothed particle hydrodynamics (sph) for complex fluid flows: Recent developments in methodology and applications

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.628772Z

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Observation 19f4d442-c112-485e-a2d8-9104b7df30cc · outbound

This paper cites A survey on sph methods in computer graphics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning A survey on sph methods in computer graphics

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.636964Z

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Observation 81e924ea-1ce1-47fc-af14-7d9ca6a8b41f · outbound

This paper cites Rogers, and Antonio Souto-Iglesias.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Rogers, and Antonio Souto-Iglesias

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.643920Z

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Observation e7dec238-2a51-43a8-a9b6-29a9dd0bdff5 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Imagenet: A large-scale hierarchical image database

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.651614Z

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Observation 6c4ce533-cc13-455b-9c3b-3b73af8f3556 · outbound

This paper cites Improving language under- standing by generative pre-training.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Improving language under- standing by generative pre-training

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.658991Z

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Observation 756c2b10-af37-4046-9294-f96b05856530 · outbound

This paper cites Mastering the game of go without human knowledge.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Mastering the game of go without human knowledge

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.666836Z

Source-reported events for the cited work

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Observation d3808161-8623-436c-990c-ae2e7338db76 · outbound

This paper cites Deep learning, volume 1.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Deep learning, volume 1

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.674276Z

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Observation 7fbb1794-7d31-4266-8402-62531535198a · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Highly accurate protein structure prediction with alphafold

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.894835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation af11c243-00a0-4087-8dc8-3dfe889d8723 · outbound

This paper cites phiflow: A differentiable pde solving framework for deep learning via physical simulations.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning phiflow: A differentiable pde solving framework for deep learning via physical simulations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.875155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f6a6f9a3-bbec-48f1-9796-1dba129eabb6 · outbound

This paper cites DiffTaichi: Differentiable Programming for Physical Simulation.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning DiffTaichi: Differentiable Programming for Physical Simulation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.704212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cf59076-6557-4b82-87b9-57eb0fa7a954 · outbound

This paper cites Apebench: A benchmark for autoregressive neural emulators of pdes.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Apebench: A benchmark for autoregressive neural emulators of pdes

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.854467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7acece1f-c2de-43ac-87eb-037e9833997c · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library, 2019.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Pytorch: An imperative style, high-performance deep learning library, 2019

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.831092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9f133d37-5c2e-4f72-9010-a0818a459dc5 · outbound

This paper cites JAX: composable transformations of Python+NumPy programs, 2018.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning JAX: composable transformations of Python+NumPy programs, 2018

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.727007Z

Source-reported events for the cited work

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Observation 2557996f-fe7e-476c-9afb-50f98ca23f70 · outbound

This paper cites Universal physics transformers.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Universal physics transformers

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.796826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 249a47f2-0ac4-4c20-bd3e-ff423164005e · outbound

This paper cites Symmetric basis convolutions for learning lagrangian fluid me- chanics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Symmetric basis convolutions for learning lagrangian fluid me- chanics

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.779647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation deca4f29-149c-4d32-8c2d-6159281ec021 · outbound

This paper cites Physics- informed neural networks (pinns) for fluid mechanics: A review, 2021.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Physics- informed neural networks (pinns) for fluid mechanics: A review, 2021

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.762531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f941f2b2-7ff8-46d9-bb35-fbf8a370a795 · outbound

This paper cites Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.745093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fd62d3df-b7f3-4f7f-a99f-b6daa3b9e949 · outbound

This paper cites Adjoint sys- tem method in shape optimization of some typical fluid flow patterns.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Adjoint sys- tem method in shape optimization of some typical fluid flow patterns

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.728106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4425eba9-a17f-4b39-8d47-08a25ba6e854 · outbound

This paper cites Deep learning methods for reynolds-averaged navier–stokes simulations of airfoil flows.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Deep learning methods for reynolds-averaged navier–stokes simulations of airfoil flows

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.711228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation da5d22cc-d11d-4d17-869b-014acd2e6403 · outbound

This paper cites Simulating cosmic structure formation with the gadget-4 code.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Simulating cosmic structure formation with the gadget-4 code

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.693532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cb29c91b-7a1e-48be-87f6-9792488cae91 · outbound

This paper cites A new class of accurate, mesh-free hydrodynamic simulation methods.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning A new class of accurate, mesh-free hydrodynamic simulation methods

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.676415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6010449b-ee5b-4f0d-bf4b-9fdbcb8a103b · outbound

This paper cites Swift: Sph with inter-dependent fine-grained tasking.Astrophysics source code library, pages ascl–1805, 2018.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Swift: Sph with inter-dependent fine-grained tasking.Astrophysics source code library, pages ascl–1805, 2018

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.658433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 31f7de5d-6730-4360-8ae4-8a70b25e58b0 · outbound

This paper cites A smoothed particle hydrodynamics mini-app for exascale.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning A smoothed particle hydrodynamics mini-app for exascale

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.639638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 071e3186-49c2-434c-86ec-2679877bcc4c · outbound

This paper cites Dualsphysics: from fluid dynamics to multiphysics problems.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Dualsphysics: from fluid dynamics to multiphysics problems

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.620731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 34162053-aa98-480e-9a78-e1a41bdd2f58 · outbound

This paper cites Sphinxsys: An open-source multi-physics and multi-resolution library based on smoothed particle hydrodynamics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Sphinxsys: An open-source multi-physics and multi-resolution library based on smoothed particle hydrodynamics

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.603974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6dacb5c9-77a3-48b1-95d9-2a595a9a22e1 · outbound

This paper cites Dinesh, Dileep Menon, Rahul Govind, Suraj Sanka, Amal S.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Dinesh, Dileep Menon, Rahul Govind, Suraj Sanka, Amal S

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.585021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 35f7fb0e-13bc-46e6-aa05-88f460cf0f2b · outbound

This paper cites SPlisHSPlasH Library.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning SPlisHSPlasH Library

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.567279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 11c149cc-edde-4384-9ad5-002baca39de7 · outbound

This paper cites JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T12:32:11.835303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71796228-91f8-4943-98b4-a41b129c4781 · outbound

This paper cites Difffr: Differentiable sph-based fluid-rigid coupling for rigid body control.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Difffr: Differentiable sph-based fluid-rigid coupling for rigid body control

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.547538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 08f4005d-9bbe-4093-8c02-e63a5dec5186 · outbound

This paper cites Warp: A high-performance python framework for gpu simulation and graphics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Warp: A high-performance python framework for gpu simulation and graphics

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.525390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9062bffd-52aa-49ae-b5ba-f36c52f27c61 · outbound

This paper cites Lagrangebench: A lagrangian fluid mechanics benchmarking suite.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Lagrangebench: A lagrangian fluid mechanics benchmarking suite

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.507680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 05a4b1ae-2e0f-48b3-b994-c890902fbcd6 · outbound

This paper cites Smoothed particle hydrodynamics and magnetohydrodynamics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Smoothed particle hydrodynamics and magnetohydrodynamics

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.489994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3d249e67-19c9-4fb1-ae15-e96fdefe8885 · outbound

This paper cites Smoothed particle hydrodynamics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Smoothed particle hydrodynamics

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.469763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1f7ae4dd-6335-4513-bb62-f597b38a46f7 · outbound

This paper cites Improving convergence in smoothed particle hydrodynamics simula- tions without pairing instability.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Improving convergence in smoothed particle hydrodynamics simula- tions without pairing instability

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.448950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ed1dbcc2-48d5-4526-b5f9-4521bdcb4574 · outbound

This paper cites Implicit incompressible sph.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Implicit incompressible sph

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.432243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2bb6c878-a7ab-40ab-bde9-9386c9efd2d0 · outbound

This paper cites Multi-level memory structures for simulating and rendering smoothed particle hydrodynamics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Multi-level memory structures for simulating and rendering smoothed particle hydrodynamics

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.411689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d52c3d7e-e5ef-4a94-a8b8-a98b8f8c74a6 · outbound

This paper cites Asph modeling of material damage and failure.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Asph modeling of material damage and failure

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.394080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 163af9f1-5106-40bc-8e6b-73e9da173635 · outbound

This paper cites A method of calculating radiative heat diffusion in particle simulations.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning A method of calculating radiative heat diffusion in particle simulations

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.377007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6acfa066-6c8b-44a4-9179-04545d439956 · outbound

This paper cites A consistent approach to particle shifting in the δ-plus-sph model.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning A consistent approach to particle shifting in the δ-plus-sph model

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.360494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 80b87a4d-a54f-45fc-9d5f-38eb9b3bc337 · outbound

This paper cites Implicit iterative particle shifting for meshless numerical schemes using kernel basis functions.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Implicit iterative particle shifting for meshless numerical schemes using kernel basis functions

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.342625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5aeea66f-4231-4a36-83a4-a135bb4f5c06 · outbound

This paper cites δ-sph model for simulating violent impact flows.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning δ-sph model for simulating violent impact flows

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.324143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a4276253-3c28-416a-90e8-0ed1fa669ef8 · outbound

This paper cites Crksph–a conservative reproducing kernel smoothed particle hydrodynamics scheme.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Crksph–a conservative reproducing kernel smoothed particle hydrodynamics scheme

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.307031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 03ae84bd-4d6c-41a7-a28f-508319524d66 · outbound

This paper cites Learning to control pdes with differentiable physics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Learning to control pdes with differentiable physics

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.289108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 01cf06f2-5dea-4ee5-97a8-6df62fe4fdec · outbound

This paper cites Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.271998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 88efe08d-c38c-4c78-a534-d7051f2c8373 · outbound

This paper cites Adjoint sensitivity analysis for differential- algebraic equations: The adjoint dae system and its numerical solution.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Adjoint sensitivity analysis for differential- algebraic equations: The adjoint dae system and its numerical solution

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.253722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b235d35f-e779-45d2-bea6-2b41ef9e3889 · outbound

This paper cites A unifying mathematical definition of particle methods.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning A unifying mathematical definition of particle methods

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.237670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2e4259ab-64f3-4686-880f-03658dc9567e · outbound

This paper cites Thuerey, B.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Thuerey, B

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.219457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8490b0e1-be52-4d58-b323-71ac339338d5 · outbound

This paper cites The δ-ale-sph model: An arbitrary lagrangian- eulerian framework for the δ-sph model with particle shifting technique.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning The δ-ale-sph model: An arbitrary lagrangian- eulerian framework for the δ-sph model with particle shifting technique

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.201617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d1d6582e-5216-466d-a336-05aff278bba0 · outbound

This paper cites Numerical diffusive terms in weakly- compressible sph schemes.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Numerical diffusive terms in weakly- compressible sph schemes

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.182772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6be19d48-a772-47c1-a4b5-9ea486953c32 · outbound

This paper cites Divergence-free smoothed particle hydrodynamics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Divergence-free smoothed particle hydrodynamics

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.163870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bb527571-73ca-4dfa-b829-a6369499eea1 · outbound

This paper cites Incompressible sph method for simulating newtonian and non- newtonian flows with a free surface.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Incompressible sph method for simulating newtonian and non- newtonian flows with a free surface

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.145967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a86a5caf-f9ef-4f07-912c-1d94b002c38c · outbound

This paper cites An optimized source term formulation for incompressible sph.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning An optimized source term formulation for incompressible sph

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.125921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8bc10af8-bd97-40cf-b614-029c20317ff3 · outbound

This paper cites A compatibly differenced total energy conserving form of sph.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning A compatibly differenced total energy conserving form of sph

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.109613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ba48175c-bf23-4a70-81b9-ef1be7fb9bb0 · outbound

This paper cites Cosmological smoothed particle hydrodynamics simulations: the entropy equation.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Cosmological smoothed particle hydrodynamics simulations: the entropy equation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.091936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1b2167dd-fcea-4dda-b208-952580966882 · outbound

This paper cites Conduction modelling using smoothed particle hydrodynamics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Conduction modelling using smoothed particle hydrodynamics

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.074529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dc04e645-7b5a-4da1-a060-0072708153c4 · outbound

This paper cites Sph compressible turbulence.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Sph compressible turbulence

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.057771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1161ce79-c4ef-4b74-83e0-2c5eb17986c3 · outbound

This paper cites Von neumann stability analysis of smoothed particle hydrodynamics—suggestions for optimal algorithms.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Von neumann stability analysis of smoothed particle hydrodynamics—suggestions for optimal algorithms

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.041214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a17fa34c-b581-4cda-a25d-4a6a190b493a · outbound

This paper cites Inviscid smoothed particle hydrodynamics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Inviscid smoothed particle hydrodynamics

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.022769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 688f5720-d241-4998-bcf0-c04521a4c707 · outbound

This paper cites A general class of lagrangian smoothed particle hydrodynamics methods and implica- tions for fluid mixing problems.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning A general class of lagrangian smoothed particle hydrodynamics methods and implica- tions for fluid mixing problems

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:13.006038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 49dc4bfd-f9b8-4c1f-af3c-9f15c7aae61e · outbound

This paper cites Semi-analytic boundary handling below particle resolution for smoothed particle hydrodynamics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Semi-analytic boundary handling below particle resolution for smoothed particle hydrodynamics

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.989457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f431e98f-6e59-4b2b-bc9b-28f22ecfb484 · outbound

This paper cites Modified dynamic boundary conditions (mdbc) for general-purpose smoothed particle hydrodynamics (sph): Application to tank sloshing, dam break and fish pass problems.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Modified dynamic boundary conditions (mdbc) for general-purpose smoothed particle hydrodynamics (sph): Application to tank sloshing, dam break and fish pass problems

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.972114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a53dcc4e-2175-421a-afdc-90065dff6ce8 · outbound

This paper cites Particle-based fluid simulation for interactive applications.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Particle-based fluid simulation for interactive applications

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.955229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f252f200-3c8c-4eaa-8704-66ecf691d394 · outbound

This paper cites Eulerian incompressible smoothed particle hydrodynamics on multiple gpus.Computer Physics Communications, 273:108263, 2022.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Eulerian incompressible smoothed particle hydrodynamics on multiple gpus.Computer Physics Communications, 273:108263, 2022

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.937830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 491aef1b-3dd6-4c61-aff4-5185d8e7f226 · outbound

This paper cites Mls pressure boundaries for divergence-free and viscous sph fluids.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Mls pressure boundaries for divergence-free and viscous sph fluids

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.920052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d8c615f6-9682-4229-a236-3aa9cc3ef196 · outbound

This paper cites An improved non-reflecting outlet boundary condition for weakly-compressible sph.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning An improved non-reflecting outlet boundary condition for weakly-compressible sph

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.903419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b89e9dd7-9b12-49f3-aad3-0e54b2d303b0 · outbound

This paper cites Multi-level-memory structures for adaptive SPH simulations.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Multi-level-memory structures for adaptive SPH simulations

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.884204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d4338dec-2cbf-438b-a7e7-fc4595a4d9dc · outbound

This paper cites A hybrid framework for fluid flow simulations: Combining sph with machine learning.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning A hybrid framework for fluid flow simulations: Combining sph with machine learning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.866771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c432f9ae-841f-4cc5-a38e-9084198880c4 · outbound

This paper cites Splinecnn: Fast geometric deep learning with continuous b-spline kernels.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Splinecnn: Fast geometric deep learning with continuous b-spline kernels

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.839592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 992969da-a40a-46a4-bf45-156c30cebca6 · outbound

This paper cites Efficient coding of the minimum image convention.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Efficient coding of the minimum image convention

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.820405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ac32a648-d5e7-4599-9dcc-ee6aba63e87f · outbound

This paper cites Constrained neighbor lists for sph-based fluid simulations.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Constrained neighbor lists for sph-based fluid simulations

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.803121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4e2ab71b-f1f4-496b-8291-6d4e3d1c75a7 · outbound

This paper cites an unresolved cited work.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:32:12.784005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ce27f395-a97b-4f61-9edd-112af624fca7 · outbound

This paper cites The complexity of partial derivatives.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning The complexity of partial derivatives

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.765833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6fc1be48-045b-4311-b5cf-f66edbba4b3b · outbound

This paper cites Kingma and Jimmy Ba.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Kingma and Jimmy Ba

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.740756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3e385926-717b-4e28-a3b0-71cd39b0cf51 · outbound

This paper cites Learnable fourier features for multi- dimensional spatial positional encoding.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Learnable fourier features for multi- dimensional spatial positional encoding

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.717180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 00ab3ad5-13b9-45e2-97c8-fde28406d8d0 · outbound

This paper cites Smith, Ayya Alieva, Qing Wang, Michael P.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Smith, Ayya Alieva, Qing Wang, Michael P

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.696017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 066baef7-c227-459f-bc9b-e0e06fcf9a64 · outbound

This paper cites Worrall, and Max Welling.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Worrall, and Max Welling

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.674307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c5478e65-fe64-4878-b2df-fc1ee0ac4dc3 · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Fourier features let networks learn high frequency functions in low dimensional domains

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.639240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a1435b48-b611-4a9d-ae53-bb29013d80f6 · outbound

This paper cites Differentiability in unrolled training of neural physics simulators on transient dynamics.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Differentiability in unrolled training of neural physics simulators on transient dynamics

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.614810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4dea4732-2ecd-4479-a8e7-3c921ac15903 · outbound

This paper cites Diehl, G.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Diehl, G

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.582604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a01ecc38-da05-4ca8-839f-afedb6c12845 · outbound

This paper cites Infinite continuous adaptivity for incom- pressible SPH.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Infinite continuous adaptivity for incom- pressible SPH

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.557792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 60ef4931-a5a9-4ba9-8e0e-f827dbc8d534 · outbound

This paper cites Fast and accurate sph modelling of 3d complex wall boundaries in viscous and non viscous flows.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Fast and accurate sph modelling of 3d complex wall boundaries in viscous and non viscous flows

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.529635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1af80a10-f5b1-4c28-bc37-af906e2c6c13 · outbound

This paper cites Versatile rigid-fluid coupling for incompressible sph.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Versatile rigid-fluid coupling for incompressible sph

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.502907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4f09db75-0bdd-4343-8694-8f9411f02d2b · outbound

This paper cites Unified semi-analytical wall boundary conditions applied to 2-d incompressible sph.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Unified semi-analytical wall boundary conditions applied to 2-d incompressible sph

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.482253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9f0281ef-7d56-4af1-8d32-d20cdfb1eb6a · outbound

This paper cites an unresolved cited work.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:32:12.460045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2c4b5e85-16fd-4e39-a091-8396cb1b825c · outbound

This paper cites Simulating free surface flows with sph.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Simulating free surface flows with sph

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:32:12.434398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0dde0bef-791d-4fad-b0ce-06a4298e36fe · outbound

This paper cites an unresolved cited work.

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:32:12.402882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers