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Researchers advance Physics-Informed Neural Networks for complex scientific modeling

By PulseAugur Editorial · [6 sources] ·

Researchers have developed novel physics-informed neural networks (PINNs) to tackle complex differential equations. One approach, Pseudo-differential-enhanced PINNs, utilizes Fourier transforms for faster and more efficient training, improving fidelity and handling fractional derivatives. Another method, Meta-Inverse PINNs, reformulates inverse modeling as a meta-learning problem to enhance sample efficiency and generalization for high-dimensional ordinary differential equations, demonstrating success in pharmacokinetic models. AI

IMPACT These advancements in PINNs could accelerate scientific discovery by enabling more accurate and efficient modeling of complex dynamical systems.

RANK_REASON This cluster contains multiple arXiv papers detailing new research and methods in physics-informed neural networks.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

Researchers advance Physics-Informed Neural Networks for complex scientific modeling

COVERAGE [6]

  1. arXiv cs.LG TIER_1 English(EN) · Reza Pirayeshshirazinezhad ·

    Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning

    arXiv:2605.05217v1 Announce Type: new Abstract: We propose a self-supervised physics-informed neural network (PINN) framework that adaptively balances physics-based and data-driven supervision for scientific machine learning under data scarcity. Unlike prior PINNs that rely on fi…

  2. arXiv cs.LG TIER_1 English(EN) · Dimitrios G. Patsatzis, Nikolaos Kazantzis, Ioannis G. Kevrekidis, Lucia Russo, Constantinos Siettos ·

    Invariant Manifolds of Discrete-time Dynamical Systems with Nonlinear Exosystems via Hybrid Physics-Informed Neural Networks

    arXiv:2506.13950v2 Announce Type: replace-cross Abstract: We propose a hybrid physics-informed machine learning framework to approximate invariant manifolds (IMs) of discrete-time dynamical systems driven by exogenous autonomous dynamics (exosystems). Such systems appear in appli…

  3. arXiv cs.AI TIER_1 English(EN) · Loc Vu-Quoc, Alexander Humer ·

    Partial-differential-algebraic equations of nonlinear dynamics by Physics-Informed Neural-Network: (I) Operator splitting and framework assessment

    arXiv:2408.01914v4 Announce Type: replace-cross Abstract: Several forms for constructing novel physics-informed neural-networks (PINN) for the solution of partial-differential-algebraic equations based on derivative operator splitting are proposed, using the nonlinear Kirchhoff r…

  4. arXiv cs.LG TIER_1 English(EN) · Zhao Wei, Kenneth Hor Cheng Koh, Sheng Yuan Chin, James Chun Yip Chan, Chin Chun Ooi, Yew-Soon Ong ·

    Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations

    arXiv:2605.03511v1 Announce Type: new Abstract: Solving inverse problems in dynamical systems governed by high-dimensional coupled ordinary differential equations (ODEs) is a ubiquitous challenge in scientific machine learning. In many real-world applications, researchers seek to…

  5. arXiv cs.LG TIER_1 English(EN) · Andrew Gracyk ·

    Pseudo-differential-enhanced physics-informed neural networks

    arXiv:2602.14663v2 Announce Type: replace Abstract: We present pseudo-differential enhanced physics-informed neural networks (PINNs), an extension of gradient enhancement but in Fourier space. Gradient enhancement of PINNs dictates that the PDE residual is taken to a higher diffe…

  6. arXiv cs.AI TIER_1 English(EN) · Yew-Soon Ong ·

    Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations

    Solving inverse problems in dynamical systems governed by high-dimensional coupled ordinary differential equations (ODEs) is a ubiquitous challenge in scientific machine learning. In many real-world applications, researchers seek to uncover unknown parameters or model unknown dyn…

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