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Researchers advance Physics-Informed Neural Networks for complex scientific modeling

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 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

Summary written by gemini-2.5-flash-lite from 6 sources. How we write summaries →

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 →

COVERAGE [6]

  1. arXiv cs.LG TIER_1 · 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 · 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 · 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 · 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 · 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 · 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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