Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations | Paper Citation Record

Observation 3c2b46e4-d845-4218-920a-b189a9d8aeb8 · outbound

This paper cites an unresolved cited work.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.295400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0575ba8-4a1c-4c26-9b76-597627f8650b · outbound

This paper cites Scientific machine learning through physics–informed neural networks: Where we are and what’s next.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Scientific machine learning through physics–informed neural networks: Where we are and what’s next

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.300448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86efb601-5bc4-4353-8bd8-55781871b565 · outbound

This paper cites Physics- informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Physics- informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.304669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74b42f72-ee37-4c1d-aafc-d01952992e00 · outbound

This paper cites Nsfnets (navier-stokes flow nets): Physics- informed neural networks for the incompressible navier-stokes equations.Journal of Computational Physics, 426:109951, 2021.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Nsfnets (navier-stokes flow nets): Physics- informed neural networks for the incompressible navier-stokes equations.Journal of Computational Physics, 426:109951, 2021

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.644074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bef0ba5d-8522-41d7-ac71-add6463c4ac9 · outbound

This paper cites Artificial neural networks for solving ordinary and partial differential equations.IEEE transactions on neural networks, 9(5):987–1000, 1998.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Artificial neural networks for solving ordinary and partial differential equations.IEEE transactions on neural networks, 9(5):987–1000, 1998

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.315601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3ed738e-dd5f-49ff-b498-d5d8d02176e5 · outbound

This paper cites Cambridge University Press, 2023.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Cambridge University Press, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.622663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e2a95540-2521-484f-b7a1-3d68bb1ae6bc · outbound

This paper cites Uncovering near-wall blood flow from sparse data with physics-informed neural networks.Physics of Fluids, 33(7):071905, 2021.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Uncovering near-wall blood flow from sparse data with physics-informed neural networks.Physics of Fluids, 33(7):071905, 2021

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.611901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c3777d65-4a77-4933-9ab2-5f99218109fb · outbound

This paper cites Mostajeran and R.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Mostajeran and R

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.602299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dfe2f2fd-63f3-43cd-9a61-dfcea5db47cf · outbound

This paper cites an unresolved cited work.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:33:08.592237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ec438df4-2258-459a-ba6b-9f2a4016271e · outbound

This paper cites On the spectral bias of neural networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations On the spectral bias of neural networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.339036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af5b174f-5688-44c9-a61b-e1cdcdf07947 · outbound

This paper cites Frequency principle: Fourier analysis sheds light on deep neural networks.Communications in Computational Physics, 28(5):1746–1767, 2020.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Frequency principle: Fourier analysis sheds light on deep neural networks.Communications in Computational Physics, 28(5):1746–1767, 2020

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.573405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 43099894-d872-4cf3-9d2b-c5ae76723e6f · outbound

This paper cites Multi-scale deep neural networks for solving high dimensional pdes.Neural Information Processing Systems, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Multi-scale deep neural networks for solving high dimensional pdes.Neural Information Processing Systems, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.560547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2ff08f03-9478-4dd7-b236-f59dc59d4888 · outbound

This paper cites Fourier neural operator for parametric partial differential equations.International Conference on Learning Representations, 2021.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Fourier neural operator for parametric partial differential equations.International Conference on Learning Representations, 2021

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.548586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a19dbf7e-9444-4748-b207-d54780624cec · outbound

This paper cites an unresolved cited work.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.359573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3338a1bb-6731-4f13-85ca-7d7a7d450211 · outbound

This paper cites Self-scalable tanh (stan): Multi-scale solutions for physics-informed neural networks.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12):15588–15603, 2023.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Self-scalable tanh (stan): Multi-scale solutions for physics-informed neural networks.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12):15588–15603, 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.527866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ffb264a2-22c7-4de9-bc33-6c4a2d3a1828 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations KAN: Kolmogorov-Arnold Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.367417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9efc67e-97be-42d2-9a6c-613bda849de8 · outbound

This paper cites Challenges in Training PINNs: A Loss Landscape Perspective.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Challenges in Training PINNs: A Loss Landscape Perspective

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.371834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c7da5a8-ad4b-4124-b76f-3db0b999e7a5 · outbound

This paper cites Kronecker neural networks overcome spectral bias for pinn-based wavefield computation.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Kronecker neural networks overcome spectral bias for pinn-based wavefield computation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.516527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4cd3d79f-50a4-4f55-9018-4f92484770ad · outbound

This paper cites On the expressiveness and spectral bias of KANs.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations On the expressiveness and spectral bias of KANs

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.382303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23f568a1-fe6f-4d9b-8971-ea4b0401f850 · outbound

This paper cites an unresolved cited work.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:33:08.503492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f5a018e6-d944-4634-9d27-74aba062e146 · outbound

This paper cites The spectral bias of polynomial neural networks.International Conference on Learning Representations, 2022.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations The spectral bias of polynomial neural networks.International Conference on Learning Representations, 2022

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.484961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5f87cd72-c5ea-49e3-bcbd-411ef93e7a42 · outbound

This paper cites Extrapolation and spectral bias of neural nets with hadamard product: a polynomial net study.Advances in neural information processing systems, 35:26980–26993, 2022.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Extrapolation and spectral bias of neural nets with hadamard product: a polynomial net study.Advances in neural information processing systems, 35:26980–26993, 2022

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.471332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dacecccb-3acc-47a7-a7ad-3a21df792bc8 · outbound

This paper cites On the Activation Function Dependence of the Spectral Bias of Neural Networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations On the Activation Function Dependence of the Spectral Bias of Neural Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.403310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00ebe1d1-0f19-4b93-86b1-0d3f2ead8832 · outbound

This paper cites an unresolved cited work.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:33:08.457335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c99a8736-a0c9-41ef-9902-749a2f45ab0c · outbound

This paper cites SIAM, 2000.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations SIAM, 2000

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.445010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b7c189d4-595a-439e-a4dd-8005329bf3de · outbound

This paper cites Elsevier, 2000.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Elsevier, 2000

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.432898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2ec689d1-36d3-4a9b-b8f5-4882e1a73d3b · outbound

This paper cites Self-adaptive physics-informed neural networks.Journal of Computational Physics, 474:111722, 2023.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Self-adaptive physics-informed neural networks.Journal of Computational Physics, 474:111722, 2023

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.417966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7db63f6-d0a3-4177-ac68-0ad0394e5108 · outbound

This paper cites Binary structured physics-informed neural networks for solving equations with rapidly changing solutions.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Binary structured physics-informed neural networks for solving equations with rapidly changing solutions

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.413958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e7952d0a-f172-4e9d-a653-db8390aa3470 · outbound

This paper cites Multilevel domain decomposition- based architectures for physics-informed neural networks.Computer Methods in Applied Mechanics and Engi- neering, 429:117116, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Multilevel domain decomposition- based architectures for physics-informed neural networks.Computer Methods in Applied Mechanics and Engi- neering, 429:117116, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.402357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b80991c2-3daf-407c-ae07-5864b1688f94 · outbound

This paper cites Adaptive sampling points based multi-scale residual network for solving partial differential equations.Computers & Mathematics with Applications, 169:223–236, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Adaptive sampling points based multi-scale residual network for solving partial differential equations.Computers & Mathematics with Applications, 169:223–236, 2024

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.429170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ae88b7d-7e06-4644-bdca-ec87ba1d9e8e · outbound

This paper cites an unresolved cited work.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:33:08.376991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1736d646-514c-43d1-91f7-1289258ccbc1 · outbound

This paper cites Exact enforcement of temporal continuity in sequential physics-informed neural networks.Computer Methods in Applied Mechanics and Engineering, 430:117197, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Exact enforcement of temporal continuity in sequential physics-informed neural networks.Computer Methods in Applied Mechanics and Engineering, 430:117197, 2024

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.437881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e0abc47-f229-426c-8902-67b8d5426668 · outbound

This paper cites Adaptive activation functions accelerate convergence in deep and physics-informed neural networks.Journal of Computational Physics, 404:109136, 2020.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Adaptive activation functions accelerate convergence in deep and physics-informed neural networks.Journal of Computational Physics, 404:109136, 2020

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.444591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c28bc77-0359-40a4-a5c8-97c57f834433 · outbound

This paper cites A practical pinn framework for multi-scale problems with multi-magnitude loss terms.Journal of Computational Physics, 510:113112, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations A practical pinn framework for multi-scale problems with multi-magnitude loss terms.Journal of Computational Physics, 510:113112, 2024

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.449786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c292e36-b55a-481b-8938-3bd4451a6314 · outbound

This paper cites an unresolved cited work.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:33:08.337262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ae86a690-b904-4ad5-ae50-8dafaa748fd1 · outbound

This paper cites Finite element interpolated neural networks for solving forward and inverse problems.Computer Methods in Applied Mechanics and Engineering, 418:116505, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Finite element interpolated neural networks for solving forward and inverse problems.Computer Methods in Applied Mechanics and Engineering, 418:116505, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.327297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6197243e-3f7d-4259-8b25-0f8117f55ea2 · outbound

This paper cites Loss-attentional physics-informed neural networks.Journal of Computational Physics, 501:112781, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Loss-attentional physics-informed neural networks.Journal of Computational Physics, 501:112781, 2024

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.464668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27cee318-89e5-423c-a51e-2e3993c666bc · outbound

This paper cites Efficient physics-informed neural networks using hash encoding.Journal of Computational Physics, 501:112760, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Efficient physics-informed neural networks using hash encoding.Journal of Computational Physics, 501:112760, 2024

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.310806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 771c74af-28a3-49cf-82ca-090f1b47d83e · outbound

This paper cites Pdebench: An extensive benchmark for scientific machine learning.Advances in Neural Information Processing Systems, 35:1596–1611, 2022.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Pdebench: An extensive benchmark for scientific machine learning.Advances in Neural Information Processing Systems, 35:1596–1611, 2022

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.474150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b0a7a30-8986-4689-8088-3f27c74929ae · outbound

This paper cites Data-driven physics-informed neural networks: A digital twin perspective.Computer Methods in Applied Mechanics and Engineering, 428:117075, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Data-driven physics-informed neural networks: A digital twin perspective.Computer Methods in Applied Mechanics and Engineering, 428:117075, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.291383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 901941c1-763c-4f5f-948e-db835456a946 · outbound

This paper cites Learnable activation functions in physics-informed neural networks for solving partial differential equations, 2025.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Learnable activation functions in physics-informed neural networks for solving partial differential equations, 2025

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.278928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation da53e342-4bf1-4347-9afe-0d4c592d138e · outbound

This paper cites A survey on modern trainable activation functions.Neural Networks, 138:14–32, 2021.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations A survey on modern trainable activation functions.Neural Networks, 138:14–32, 2021

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.261856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2906d175-0266-4bf8-b5a3-d5913d8701c8 · outbound

This paper cites Searching for Activation Functions.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Searching for Activation Functions

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.491534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab389f93-47d0-447b-bdc1-14e8dc7fbd64 · outbound

This paper cites Adaptive blending units: Trainable activation functions for deep neural networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Adaptive blending units: Trainable activation functions for deep neural networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.247368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a4e34f3a-2810-412f-9fad-f212973795d4 · outbound

This paper cites Learning Specialized Activation Functions for Physics-informed Neural Networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Learning Specialized Activation Functions for Physics-informed Neural Networks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.502417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c9dfbae-6c97-46e1-b46f-fcc00fa96899 · outbound

This paper cites KAN versus MLP on Irregular or Noisy Functions.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations KAN versus MLP on Irregular or Noisy Functions

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.506610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1264d821-55c2-4d1a-b109-9a2d4a7b0144 · outbound

This paper cites A comprehen- sive and fair comparison between mlp and kan representations for differential equations and operator networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations A comprehen- sive and fair comparison between mlp and kan representations for differential equations and operator networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.234579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d8ad1383-b7f8-49fc-8362-04e2a284862c · outbound

This paper cites Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.514743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25fb16f0-b5fc-46d3-9a1e-0102e2b73d64 · outbound

This paper cites Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.519050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a008b487-180a-4e7b-8ff0-cb6f4b000ed6 · outbound

This paper cites Approximation by superpositions of a sigmoidal function.Mathematics of Control, Signals and Systems, 2(4):303–314, 1989.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Approximation by superpositions of a sigmoidal function.Mathematics of Control, Signals and Systems, 2(4):303–314, 1989

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.524750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba5e3dda-64c1-46cf-9dcf-0a4ec4ff3723 · outbound

This paper cites an unresolved cited work.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:33:08.213103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2ab84a6d-eba6-4b12-b8a7-b25bd08521a8 · outbound

This paper cites On calculating with b-splines.Journal of Approximation theory, 6(1):50–62, 1972.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations On calculating with b-splines.Journal of Approximation theory, 6(1):50–62, 1972

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.202168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6aa44c77-78ba-48ab-990b-39b5e0f2fa10 · outbound

This paper cites Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.538028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56e03286-b225-49bc-a54a-a21aaf99755e · outbound

This paper cites Kolmogorov-Arnold Networks are Radial Basis Function Networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Kolmogorov-Arnold Networks are Radial Basis Function Networks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.542829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aca8017e-2641-4499-92b7-dde330271277 · outbound

This paper cites BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold Networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold Networks

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.548728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 549ffb2b-415c-4612-8617-6f2c5ff4ab16 · outbound

This paper cites Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.554974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 264f1bad-20fa-43ea-bc7c-b376b0fd457c · outbound

This paper cites Wav-KAN: Wavelet Kolmogorov-Arnold Networks.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Wav-KAN: Wavelet Kolmogorov-Arnold Networks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.563897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72bc634c-6fc8-4a04-ace9-18ae66c2700c · outbound

This paper cites Unveiling the Power of Wavelets: A Wavelet-based Kolmogorov-Arnold Network for Hyperspectral Image Classification.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Unveiling the Power of Wavelets: A Wavelet-based Kolmogorov-Arnold Network for Hyperspectral Image Classification

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.569281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69c2ca4d-6844-43d7-8aa4-350da4271176 · outbound

This paper cites SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.574830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b33ad2c-4dc7-4109-8700-2cc4146ef099 · outbound

This paper cites Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.580779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8726aa63-890c-44bb-abff-d8698eb3573c · outbound

This paper cites Understanding and mitigating gradient flow pathologies in physics-informed neural networks.SIAM Journal on Scientific Computing, 43(5):A3055–A3081, 2021.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Understanding and mitigating gradient flow pathologies in physics-informed neural networks.SIAM Journal on Scientific Computing, 43(5):A3055–A3081, 2021

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.188730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9c1cf206-8845-46ef-af94-79acc8a9135e · outbound

This paper cites When and why pinns fail to train: A neural tangent kernel perspective.Journal of Computational Physics, 449:110768, 2022.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations When and why pinns fail to train: A neural tangent kernel perspective.Journal of Computational Physics, 449:110768, 2022

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.595245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a121a4f7-4545-4132-9be7-8bbd60fcab5c · outbound

This paper cites Multi-objective loss balancing for physics-informed deep learning.Computer Methods in Applied Mechanics and Engineering, 439:117914, 2025.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Multi-objective loss balancing for physics-informed deep learning.Computer Methods in Applied Mechanics and Engineering, 439:117914, 2025

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.599969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation deed517c-a88d-4b45-b824-86640a3ed4b0 · outbound

This paper cites Residual- based attention in physics-informed neural networks.Computer Methods in Applied Mechanics and Engineering, 421:116805, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Residual- based attention in physics-informed neural networks.Computer Methods in Applied Mechanics and Engineering, 421:116805, 2024

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.606265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10535461-eca5-4c0e-bd36-6df534613136 · outbound

This paper cites Simple yet effective adaptive activation functions for physics-informed neural networks.Computer Physics Communications, page 109428, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Simple yet effective adaptive activation functions for physics-informed neural networks.Computer Physics Communications, page 109428, 2024

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.153728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6797d29a-69dc-487c-8d07-537392e48406 · outbound

This paper cites Adaptive-sampling physics-informed neural network for viscoacoustic wavefield simulation.IEEE Geoscience and Remote Sensing Letters, 2024.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Adaptive-sampling physics-informed neural network for viscoacoustic wavefield simulation.IEEE Geoscience and Remote Sensing Letters, 2024

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.142454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0638aab5-a78d-4afb-aed8-a3246ca79dd7 · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks.Advances in neural information processing systems, 31, 2018.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Neural tangent kernel: Convergence and generalization in neural networks.Advances in neural information processing systems, 31, 2018

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.622688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58162db9-8fb3-452c-bd1a-38386fb20fdb · outbound

This paper cites Wide neural networks of any depth evolve as linear models under gradient descent.Advances in neural information processing systems, 32, 2019.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Wide neural networks of any depth evolve as linear models under gradient descent.Advances in neural information processing systems, 32, 2019

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.627401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 656d80b5-63fd-4b84-95f7-c094c238bee3 · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains.Advances in neural information processing systems, 33:7537–7547, 2020.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Fourier features let networks learn high frequency functions in low dimensional domains.Advances in neural information processing systems, 33:7537–7547, 2020

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.631467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05042ed4-2f84-4d66-8f3f-dfe025e51690 · outbound

This paper cites On understanding and overcoming spectral biases of deep neural network learning methods for solving pdes.Journal of Computational Physics, 2025.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations On understanding and overcoming spectral biases of deep neural network learning methods for solving pdes.Journal of Computational Physics, 2025

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.105899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d3b9d8c3-3cf6-44c4-ad92-f59b89ce14f6 · outbound

This paper cites Reproducing activation function for deep learning.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Reproducing activation function for deep learning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.093490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a002ac7e-d955-4ff0-b1a1-a166d2607de1 · outbound

This paper cites An investigation into neural net optimization via hessian eigenvalue density.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations An investigation into neural net optimization via hessian eigenvalue density

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.643499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb81205a-9020-4fad-be04-9c07932b8dcc · outbound

This paper cites Gradient descent on neural networks typically occurs at the edge of stability.International Conference on Learning Representations, 2021.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Gradient descent on neural networks typically occurs at the edge of stability.International Conference on Learning Representations, 2021

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.069145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7ec6b88f-8d7f-48e2-a2f0-93f55435bf11 · outbound

This paper cites Negative eigenvalues of the hessian in deep neural networks.International Conference on Learning Representations, 2019.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Negative eigenvalues of the hessian in deep neural networks.International Conference on Learning Representations, 2019

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.054508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f0b1b739-00bd-4731-b7f7-3ed0a04dcb01 · outbound

This paper cites Hessian eigenspectra of more realistic nonlinear models.Advances in Neural Information Processing Systems, 34:20104–20117, 2021.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Hessian eigenspectra of more realistic nonlinear models.Advances in Neural Information Processing Systems, 34:20104–20117, 2021

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.658010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 426281de-ec1e-404d-b060-f8d3396da3b1 · outbound

This paper cites Sharpness-aware minimization for efficiently improving generalization.International Conference on Learning Representations, 2021.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Sharpness-aware minimization for efficiently improving generalization.International Conference on Learning Representations, 2021

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.035173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ea2d9deb-4fee-43ef-9e4d-b6be91689737 · outbound

This paper cites On the maximum hessian eigenvalue and generalization.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations On the maximum hessian eigenvalue and generalization

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-12T14:33:07.669461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a3029d3-c16a-48dd-9e25-c4a4e14535a5 · outbound

This paper cites Neural networks-tricks of the trade second edition.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Neural networks-tricks of the trade second edition

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:08.011538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c84d6a4e-2e79-4459-a202-214e3e9e2114 · outbound

This paper cites Pyhessian: Neural networks through the lens of the hessian.

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations Pyhessian: Neural networks through the lens of the hessian

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:33:07.996868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers