arXiv AI July 17, 2026 By Nilay Anurag, Shital Adhikari, Taniya Kapoor, Nikhil Muralidhar

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks

arXiv:2607. 14233v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have had a broad research impact in modeling domains governed by partial differential equations (PDE).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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arXiv Machine Learning

Jun 5

On the training of physics-informed neural operators for solving parametric partial differential equations

arXiv:2606. 06164v1 Announce Type: new Abstract: Physics-informed neural operators (PINOs) aim to learn solution operators for partial differential equations by using the governing physics as supervision, rather than relying solely on paired input-output simulation data.

By Nanxi Chen, Chuanjie Cui, Airong Chen, Sifan Wang, Rujin Ma

llms computer-vision benchmarks

Hugging Face Trending Papers

Jun 4

On the training of physics-informed neural operators for solving parametric partial differential equations

Physics-informed neural operators (PINOs) aim to learn solution operators for partial differential equations by using the governing physics as supervision, rather than relying solely on paired input-output simulation data. By incorporating physical constraints into the training objective, PINOs combine the cross-instance generalization of neural operators with the data efficiency of physics-informed learning.

llms computer-vision benchmarks

arXiv Machine Learning

Sep 22

Learning Physics from an Imperfect Ancestor

arXiv:2609.24947v1 Announce Type: new Abstract: Neural operators evaluate parametric partial differential equations cheaply but degrade sharply outside their training distribution. Physics-informed n...

By S. Mohammad Mousavi, Teeratorn Kadeethum, Nikolaos Bouklas, Somdatta Goswami

efficiency

arXiv Machine Learning

Aug 6

Continual-Learning Physics-Informed Neural Networks for Parameterized Partial Differential Equations

arXiv:2608. 04778v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) incorporate governing equations into neural-network training and can approximate PDE solutions without requiring large observational datasets.

By Xujia Chen, Xinyue Hu, Letian Chen, Yi Liu, Wenhui Fan

benchmarks

arXiv Machine Learning

Jun 3

Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing

arXiv:2604. 07366v2 Announce Type: replace Abstract: Partial differential equations (PDEs) govern nearly every physical process in science and engineering, but solving them at scale remains prohibitively expensive.

By Yilong Dai, Shengyu Chen, Xiaowei Jia, Runlong Yu

diffusion safety

arXiv Machine Learning

Aug 31

Deflation-PINNs: Learning Multiple Solutions for PDEs and Landau-de Gennes

arXiv:2603.27936v3 Announce Type: replace-cross Abstract: Nonlinear Partial Differential Equations (PDEs) are ubiquitous in mathematical physics and engineering. Although Physics-Informed Neural Netw...

By Sean Disar\`o, Ruma Rani Maity, Aras Bacho

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On the training of physics-informed neural operators for solving parametric partial differential equations

On the training of physics-informed neural operators for solving parametric partial differential equations

Learning Physics from an Imperfect Ancestor

Continual-Learning Physics-Informed Neural Networks for Parameterized Partial Differential Equations

Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing

Deflation-PINNs: Learning Multiple Solutions for PDEs and Landau-de Gennes