Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:57:03.240329Z
Paper Citation Record · LEDGER
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation
As of 13 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 1 inbound Pith citation observation for arXiv:2505.16971.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T18:15:11.184267Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T18:15:13.495302Z
Reference resolution
64 of 64 outbound references displayed
- verified exact1
- verified fuzzy59
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b73b14ae-6408-4913-8a49-c1066c020cbd · outbound
This paper cites A three-dimensional constitutive model for the large stretch behavior of rubber elastic materials.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation A three-dimensional constitutive model for the large stretch behavior of rubber elastic materials
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:15.368220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ebe5b3ee-f9bc-4e5c-9f31-f516bb6bc680 · outbound
This paper cites The material-point method for granular materials.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation The material-point method for granular materials
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:15.224409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 15e88814-059d-4465-a960-a8f361aa6d4b · outbound
This paper cites Combining differentiable pde solvers and graph neu- ral networks for fluid flow prediction.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Combining differentiable pde solvers and graph neu- ral networks for fluid flow prediction
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:15.052069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 60a97d16-333c-49e7-ad70-abb3377e982b · outbound
This paper cites End-to- end object detection with transformers.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation End-to- end object detection with transformers
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:56:58.415219Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 78fcc308-1ec5-47ce-9829-56bfbc1615fa · outbound
This paper cites Vir- tual elastic objects.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Vir- tual elastic objects
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:14.919490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e9dbf252-6862-4a8c-ac85-a0204aacf7d8 · outbound
This paper cites Bubbles, drops, and particles in non- Newtonian fluids.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Bubbles, drops, and particles in non- Newtonian fluids
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:14.812755Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 9a5c8421-4297-4e8d-8001-2ddec28e1da2 · outbound
This paper cites End-to-end differen- tiable physics for learning and control.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation End-to-end differen- tiable physics for learning and control
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:14.719958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e073cc38-f737-4d45-9fad-c992d05de1b9 · outbound
This paper cites A differentiable physics engine for deep learning in robotics.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation A differentiable physics engine for deep learning in robotics
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:14.605775Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 091c0884-13f3-401f-9e8c-db1b75a0fd4d · outbound
This paper cites Functional optimization of flu- idic devices with differentiable stokes flow.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Functional optimization of flu- idic devices with differentiable stokes flow
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:14.472819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8bd1b8a8-7a6c-419a-8710-46a01604b9de · outbound
This paper cites Diffpd: Differentiable projective dynamics.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Diffpd: Differentiable projective dynamics
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:14.334413Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation a2d171b4-a62d-4db9-8f3d-9f76405f20a1 · outbound
This paper cites Silly rubber: an implicit material point method for simulating non-equilibrated viscoelastic and elastoplastic solids.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Silly rubber: an implicit material point method for simulating non-equilibrated viscoelastic and elastoplastic solids
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:14.173970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 900e43a7-c91a-4482-a8fe-e4154c5c7a94 · outbound
This paper cites Elasticity of soft tissues in simple elongation.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Elasticity of soft tissues in simple elongation
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:14.010721Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e0f8598a-2e8e-4afb-8a65-d736152b8e8a · outbound
This paper cites Add: Analytically differentiable dynamics for multi-body systems with frictional contact.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Add: Analytically differentiable dynamics for multi-body systems with frictional contact
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:13.813148Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2337702f-bba4-477f-bbc8-21a0d0ceb33b · outbound
This paper cites Deformable part models are convolutional neural net- works.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Deformable part models are convolutional neural net- works
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:13.687369Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2646b83a-417a-4a31-a926-e0cd4efa3234 · outbound
This paper cites Forward flow for novel view synthesis of dynamic scenes.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Forward flow for novel view synthesis of dynamic scenes
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:13.557853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8790edb1-8976-4101-a754-476e8a0d9209 · outbound
This paper cites Real2sim: Visco-elastic parameter estimation from dynamic motion.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Real2sim: Visco-elastic parameter estimation from dynamic motion
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:13.436769Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5ab3251f-1272-494b-8280-8d1e15896209 · outbound
This paper cites Learning Physics-Consistent Material Behavior from Dynamic Displacements.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Learning Physics-Consistent Material Behavior from Dynamic Displacements
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T14:57:03.535526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8e715453-92a4-4fbe-bb32-5df64f84d835 · outbound
This paper cites Chainqueen: A real-time differen- tiable physical simulator for soft robotics.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Chainqueen: A real-time differen- tiable physical simulator for soft robotics
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:13.269980Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3933f260-b5fd-4cdf-88fa-3f049a270e97 · outbound
This paper cites Difftaichi: Differentiable programming for physical simulation.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Difftaichi: Differentiable programming for physical simulation
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:13.025193Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 6963acb4-3707-4a4d-9780-75fc71f18a3b · outbound
This paper cites Learning constitutive relations from indirect observations us- ing deep neural networks.Journal of Computational Physics, 416, 2020.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Learning constitutive relations from indirect observations us- ing deep neural networks.Journal of Computational Physics, 416, 2020
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:12.861537Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 29b7e8a2-e30a-4214-994c-56eff32edb9b · outbound
This paper cites Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:12.684955Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation dfabfe74-6403-446e-a25e-e652ab845723 · outbound
This paper cites Plasticinelab: A soft-body manipulation benchmark with differentiable physics.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Plasticinelab: A soft-body manipulation benchmark with differentiable physics
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:12.556777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 7f2c31ef-99c8-479e-b9d9-a362427959bc · outbound
This paper cites The affine particle-in-cell method.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation The affine particle-in-cell method
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:12.307544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 20725c3f-da3b-4339-8211-0c106e36af87 · outbound
This paper cites Learning category-specific mesh reconstruc- tion from image collections.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Learning category-specific mesh reconstruc- tion from image collections
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:12.131926Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation dc7e7f60-ac31-41bb-9cd0-86479419d5fa · outbound
This paper cites Physics-informed ma- chine learning.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Physics-informed ma- chine learning
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:11.946489Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation df7a1a55-641c-4d67-9e3a-7244ad95eac0 · outbound
This paper cites 3d gaussian splatting for real-time radiance field rendering.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation 3d gaussian splatting for real-time radiance field rendering
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:57:00.265220Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f3dcaa62-b0dc-4482-bef6-9ec265809210 · outbound
This paper cites Segment any- thing.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Segment any- thing
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:11.711479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation cdbf7069-d19c-466f-8bd1-6343f2b49af7 · outbound
This paper cites Drucker-prager elastoplasticity for sand animation.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Drucker-prager elastoplasticity for sand animation
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:11.459295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation fdbf39ad-d8e1-45c3-a331-47b7d7d59637 · outbound
This paper cites Polyconvex anisotropic hy- perelasticity with neural networks.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Polyconvex anisotropic hy- perelasticity with neural networks
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:11.223040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 0787c4be-3361-4333-9116-022da6e3e53c · outbound
This paper cites Plasticitynet: Learning to simulate metal, sand, and snow for optimization time integration.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Plasticitynet: Learning to simulate metal, sand, and snow for optimization time integration
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:10.982169Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 52394e76-907a-4110-adef-485a8d86012b · outbound
This paper cites Pac-nerf: Physics augmented continuum neural ra- diance fields for geometry-agnostic system identification.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Pac-nerf: Physics augmented continuum neural ra- diance fields for geometry-agnostic system identification
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:10.667863Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ef17cddf-5b99-4699-bdb8-c543f618799c · outbound
This paper cites Dynibar: Neural dynamic image-based rendering.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Dynibar: Neural dynamic image-based rendering
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:10.364677Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e542062f-5a16-4665-83ea-ca6d7fcbcf1e · outbound
This paper cites Differen- tiable cloth simulation for inverse problems.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Differen- tiable cloth simulation for inverse problems
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:10.133342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d1ba87f6-d077-4958-81f5-56b6a7dc164f · outbound
This paper cites A learning-based multiscale method and its application to inelastic impact problems.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation A learning-based multiscale method and its application to inelastic impact problems
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:09.837725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 54675fe2-a816-4803-947f-8711b70788e0 · outbound
This paper cites Soft ras- terizer: A differentiable renderer for image-based 3d reason- ing.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Soft ras- terizer: A differentiable renderer for image-based 3d reason- ing
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:09.585208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 7d71e15f-a71b-4829-9c8e-6cccaa97ea0a · outbound
This paper cites Dynamic 3d gaussians: Tracking by per- sistent dynamic view synthesis.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Dynamic 3d gaussians: Tracking by per- sistent dynamic view synthesis
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:09.291563Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4c7d2613-d73f-4071-acce-9a880a957198 · outbound
This paper cites Diffaqua: A differentiable computational design pipeline for soft underwater swimmers with shape interpolation.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Diffaqua: A differentiable computational design pipeline for soft underwater swimmers with shape interpolation
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:09.023008Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b415a77c-4f42-470e-9421-4b3a35788c57 · outbound
This paper cites Risp: Rendering-invariant state pre- dictor with differentiable simulation and rendering for cross- domain parameter estimation.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Risp: Rendering-invariant state pre- dictor with differentiable simulation and rendering for cross- domain parameter estimation
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:08.808014Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8fea23b7-2143-48be-800f-350f728cf6f1 · outbound
This paper cites Learning neural constitutive laws from motion observations for generalizable pde dynamics.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Learning neural constitutive laws from motion observations for generalizable pde dynamics
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:08.619743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4d68109c-732d-4c20-9602-90c00928a9c6 · outbound
This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Nerf: Representing scenes as neural radiance fields for view syn- thesis
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:08.349012Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 96a3517b-abca-4fd3-9a9a-ce9a3b9dca43 · outbound
This paper cites Mechanik der festen k ¨orper im plastisch- deformablen zustand.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Mechanik der festen k ¨orper im plastisch- deformablen zustand
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:08.116666Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4eff2386-bba0-41a5-a33c-30d27f8f07f6 · outbound
This paper cites Hypernerf: A higher- dimensional representation for topologically varying neural radiance fields.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Hypernerf: A higher- dimensional representation for topologically varying neural radiance fields
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:07.968480Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f0b5cf0d-c7d2-42b2-8b72-5e7b41ae00c1 · outbound
This paper cites Learning mesh-based simulation with graph networks.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Learning mesh-based simulation with graph networks
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:07.825809Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation bf2223a4-224a-43b7-95b7-4b6d4e0057f2 · outbound
This paper cites D-nerf: Neural radiance fields for dynamic scenes.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation D-nerf: Neural radiance fields for dynamic scenes
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:07.679729Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 73cef4b4-8739-4a9f-a80a-4664163e95f3 · outbound
This paper cites Differentiable simulation of soft multi-body systems.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Differentiable simulation of soft multi-body systems
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:07.518183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b7d4cfbc-7c0f-449e-99a2-0bcdcef6cb1b · outbound
This paper cites Physics-informed neural networks: A deep learning frame- work for solving forward and inverse problems involving nonlinear partial differential equations.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Physics-informed neural networks: A deep learning frame- work for solving forward and inverse problems involving nonlinear partial differential equations
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:07.373011Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ca34a664-c380-46fe-b69d-dd3cfb342dab · outbound
This paper cites Learning to simulate complex physics with graph networks.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Learning to simulate complex physics with graph networks
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:07.257619Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation daa19a77-9b6e-464d-a637-0f7ea7d63b53 · outbound
This paper cites Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:57:02.079556Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8f9d74a0-cdf0-43a0-9f48-2aa6d6e55a14 · outbound
This paper cites The elasticity of a network of long-chain molecules—ii.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation The elasticity of a network of long-chain molecules—ii
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:07.116174Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3085763f-39bb-433f-8deb-53f6fc5f6b26 · outbound
This paper cites Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:06.995402Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 197c85db-0217-4a91-9e7d-2ad4856e190f · outbound
This paper cites Component-based machine learning paradigm for discovering rate-dependent and pressure-sensitive level-set plasticity models.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Component-based machine learning paradigm for discovering rate-dependent and pressure-sensitive level-set plasticity models
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:06.868420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f6a71640-dfbf-42d9-82c0-0c0184fe7ffa · outbound
This paper cites Geometric learning for computational mechanics part ii: Graph embedding for interpretable multiscale plasticity.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Geometric learning for computational mechanics part ii: Graph embedding for interpretable multiscale plasticity
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:06.763358Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4da6f4a6-aeae-4430-a457-cd55d861a297 · outbound
This paper cites Geo- metric deep learning for computational mechanics part i: Anisotropic hyperelasticity.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Geo- metric deep learning for computational mechanics part i: Anisotropic hyperelasticity
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:06.638071Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 0d925461-75c8-401e-ba4e-0bac35cfd2c2 · outbound
This paper cites Molecular dynamics inferred trans- fer learning models for finite-strain hyperelasticity of mon- oclinic crystals: Sobolev training and validations against physical constraints.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Molecular dynamics inferred trans- fer learning models for finite-strain hyperelasticity of mon- oclinic crystals: Sobolev training and validations against physical constraints
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:06.421679Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ae34e54f-44b5-4bd3-a720-f705c6a3ab50 · outbound
This paper cites Learning elastic constitutive material and damping models.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Learning elastic constitutive material and damping models
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:06.195139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 7df79dd6-6328-4e69-8cb6-459e7373c12f · outbound
This paper cites A multiscale multi- permeability poroplasticity model linked by recursive ho- mogenizations and deep learning.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation A multiscale multi- permeability poroplasticity model linked by recursive ho- mogenizations and deep learning
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:05.936608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 85f04e4b-74b1-42d9-abe7-aabe852efcf2 · outbound
This paper cites Fluid- lab: A differentiable environment for benchmarking com- plex fluid manipulation.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Fluid- lab: A differentiable environment for benchmarking com- plex fluid manipulation
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:05.657608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 7aa83a63-4eaf-453c-9c92-8c0349c03b58 · outbound
This paper cites Physgaussian: Physics- integrated 3d gaussians for generative dynamics.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Physgaussian: Physics- integrated 3d gaussians for generative dynamics
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:05.408680Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 30e5b262-a34b-4c19-b6c9-5154273065cc · outbound
This paper cites Nonlinear material design using principal stretches.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Nonlinear material design using principal stretches
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:05.128252Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c2e12319-f591-435a-86f1-8ec199229753 · outbound
This paper cites Continuum foam: A material point method for shear-dependent flows.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Continuum foam: A material point method for shear-dependent flows
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:04.835322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation bc413479-c9ec-48d9-9c54-96d0023d55db · outbound
This paper cites Physdreamer: Physics-based interac- tion with 3d objects via video generation.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Physdreamer: Physics-based interac- tion with 3d objects via video generation
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:04.583217Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2fb80271-6917-41e5-b308-29e104dd13c7 · outbound
This paper cites The ground truth state includes the position x, velocity v, affine velocity C, and deformation gradient F.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation The ground truth state includes the position x, velocity v, affine velocity C, and deformation gradient F
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:04.290714Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ffcffaa3-d650-4728-b4f5-bdea0df46992 · outbound
This paper cites an unresolved cited work.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation Unresolved cited work
Reference 63
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T14:57:04.024897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e4fcaf41-8c62-4cae-850e-1659f407a9ba · outbound
This paper cites In Material Point Method (MPM), each particle has a deformation gradient F which is projected on to the yield surface using a return mapping G.
UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation In Material Point Method (MPM), each particle has a deformation gradient F which is projected on to the yield surface using a return mapping G
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:57:03.756500Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Pith citing papers
Observation a5cdd262-91d3-4b49-9bb1-ae3c7147c1ba · inbound
Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels cites this paper.
Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T18:15:13.579546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.