Latest Physics-Informed Neural Nets Research Papers

The newest Physics-Informed Neural Nets papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Physics-Informed Neural Nets so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
  Abhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026

  Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…

• Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
  Lorenzo Longarini, Alessandro Rongoni, Simone Silenzi, Emanuele Frontoni et al. · arXiv · Jun 5, 2026

  At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters. This cold-start setting is addressed with a zero-shot pip…

• Physics-Informed Residuals for Adaptive Mesh Refinement in Finite-Difference PDE Solvers
  Henry Kasumba, Ronald Katende · arXiv · Jun 1, 2026

  Classical finite-difference solvers remain reliable tools for partial differential equations, but their efficiency depends on where mesh resolution is placed. Uniform refinement can waste degrees of freedom when solution difficulty is local…

• Eradicating Negative Transfer in Multi-Physics Foundation Models via Sparse Mixture-of-Experts Routing
  Ellwil Sharma, Arastu Sharma · arXiv · May 14, 2026

  Scaling Scientific Machine Learning (SciML) toward universal foundation models is bottlenecked by negative transfer: the simultaneous co-training of disparate partial differential equation (PDE) regimes can induce gradient conflict, unstabl…

• Prior-Free Arterial Spin Labeling Quantification via 3D Physics-Informed Neural Networks
  Alessandro Giupponi, Chiara Da Villa, Giulio Ferrazzi, Francesca Benedetta Pizzini et al. · MIDL 2026 - Short Papers Poster · May 9, 2026

  Arterial Spin Labeling parameter estimation is a non-linear inverse problem limited by low signal-to-noise ratio (SNR) and complex parameter dependencies. Traditional Variational Bayesian (VB) methods mitigate this via explicit spatial prio…

• NEUROPIA: Neural Cognitive Field Unification for Cross-Domain Dissipative Intelligence
  Samir Baladi · Zenodo (CERN European Organ... · May 9, 2026

  NEUROPIA (E-LAB-10) is the tenth and culminating Physics-Informed Artificial Intelligence (PIAI) framework of the EntropyLab research program. The framework introduces the Neural Unified Propagator (NUP), a 32-component cross-domain operato…

• Adaptive Domain Decomposition Physics-Informed Neural Networks for Traffic State Estimation with Sparse Sensor Data
  Eunhan Ka, Ludovic Leclercq, Satish V. Ukkusuri · arXiv · May 8, 2026

  Traffic state estimation from sparse fixed sensors is challenging because physics-informed neural networks (PINNs) tend to over-smooth the shockwaves admitted by the Lighthill-Whitham-Richards (LWR) model. This study proposes Adaptive Domai…

• Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries
  Rodolphe Barlogis, Ferhat Tamssaouet, Quentin Falcoz, Stéphane Grieu · arXiv · May 1, 2026

  This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network based on the DeepONet framework. We consider a 2D square domain with an inclusion of arbitrary boundary…

• An adaptive wavelet-based PINN for problems with localized high-magnitude source
  Himanshu Pandey, Ratikanta Behera · arXiv · Apr 30, 2026

  In recent years, physics-informed neural networks (PINNs) have gained significant attention for solving differential equations, although they suffer from two fundamental limitations, namely, spectral bias inherent in neural networks and los…

• Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
  Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026

  Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…

• Diffusion-Guided Feature Selection via Nishimori Temperature: Noise-Based Spectral Embedding
  Vasiliy S. Usatyuk, Denis A. Sapozhnikov, Sergey I. Egorov · arXiv · Apr 27, 2026

  We propose Noise-Based Spectral Embedding (NBSE), a physics-informed framework for selecting informative features from high-dimensional data without greedy search. NBSE constructs a sparse similarity graph on the samples and identifies the …

• A modified physics-informed neural network for microstructural scale effects on heat and mass transfer in fractional thermo-elasto-diffusive porous media
  Abhik Sur, Khaled Lotfy, Sujit Bhattacharyya, Rachaita Dutta et al. · International Communication... · Apr 27, 2026
• THERMO-NET: Neural Thermodynamic Dissipation Management for High-Entropy Physical Systems
  Samir Baladi · Open MIND · Apr 25, 2026

  We introduce THERMO-NET, a Physics-Informed Artificial Intelligence (PIAI) framework engineered to model, predict, and actively suppress irreversible entropy production in high-density computational substrates, cryogenic quantum hardware, a…

• Transferable Physics-Informed Representations via Closed-Form Head Adaptation
  Jian Cheng Wong, Isaac Yin Chung Lai, Pao-Hsiung Chiu, Chin Chun Ooi et al. · arXiv · Apr 23, 2026

  Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physical phenomena. By incorporating physical laws into the learnin…

• CERTIFICATION AND TRUSTWORTHY DEPLOYMENT OF EXPLAINABLE AND PHYSICS-INFORMED ARTIFICIAL INTELLIGENCE FOR AEROSPACE NON-DESTRUCTIVE TESTING
  Rexcharles Enyinna Donatus, LOVETH OTE UHIAH · International Journal of En... · Apr 3, 2026

  Artificial intelligence (AI) is increasingly used in aerospace non-destructive testing (NDT) for composite materials, additively manufactured components, and automated inspection systems. Despite promising detection performance, the use of …

• Physics-informed neural network for thermal property inversion of airport pavement multilayer materials under icing conditions
  Xinyuan Xing, Jianming Ling, Shifu Liu, Zefeng Tao · Construction and Building M... · Apr 3, 2026
• Enhanced Solution for the Advection–Diffusion–Reaction Equation Using the Physics-Informed Neural Network Technique
  Thabo Lekaba, Ndivhuwo Ndou, Kizito Muzhinji, Simiso Moyo · Mathematics · Apr 2, 2026

  This study focuses on the use of Physics-Informed Neural Networks (PINNs) to solve the 1D Advection–Diffusion–Reaction (ADR) equation. The performance of the PINN model is evaluated in comparison with the classical Crank–Nicolson Finite Dif…

• Damage assessment of thermal-humidity-mechanical coupling field of early-age concrete based on adaptive physics informed neural network
  Shiqi Wang, Yue Chen, Jinlong Liu, Fangzhou Lin et al. · Engineering Applications of... · Mar 29, 2026

  The crack-damage resistance of early-age concrete is affected by multiple factors such as hydration, self-drying, temperature and humidity diffusion, and material properties, which are difficult to be accurately evaluated by traditional the…

• Physics-informed hybrid digital twin framework integrating fractal contact modeling and edge-cloud artificial intelligence for dynamic thermal contact conductance prediction
  Jialan Liu, Chi Ma, Mingming Li, Jialong He et al. · International Journal of He... · Mar 27, 2026

  Accurate characterization of thermal contact conductance (TCC) at tapered roller/groove interfaces is essential for predicting thermal behavior in precision machine tools. Conventional analytical models fail to capture the nonlinear, time-v…

• AI-driven unified and modular PINN frameworks for multiphysics convection in porous media
  S. Sarthak, D. Srinivasacharya · Engineering With Computers · Mar 17, 2026
• Real-time thermal evaluation on three-phase enclosure gas insulated bus by PSO-PINN approach
  xiangyu guan, Xiaokun Chen, Xinling Xu, Di Mo et al. · Case Studies in Thermal Eng... · Mar 16, 2026

  Real-time thermal evaluation including temperature field calculation and internal heat source inversion is crucial for power equipment ampacity and safety. This study presents a particle swarm optimized physics-informed neural network (PSO-…

• Reactive Transport Modeling with Physics-Informed Machine Learning for Critical Minerals Applications
  Kripa Adhikari, Md Lal Mamud, Maruti Kumar Mudunuru, K. B. Nakshatrala · Transport in Porous Media · Mar 13, 2026
• Informed Reconstruction-Oriented Numerical Networks: A More Efficient Method for Solving Multiple Unknown Parameters of Manakov Equations
  Peng-Fei Wang, Yunzhou Sun, H. Triki, Qin Zhou · Chinese Physics Letters · Mar 13, 2026

  Abstract By simultaneously introducing a finite-difference-based numerical loss term and a clustering–reconstruction mechanism, we propose an enhanced physics-informed neural network named the informed reconstruction-oriented numerical netw…

• Critical bistability in variable cross-section metamaterials: PINN-driven transient stress recognition and adaptive energy dissipation
  Jian Zhou, Chengjun Zeng, Wei Zhao, Jinsong Leng et al. · Chemical Engineering Journal · Mar 9, 2026
• Physics-informed neural network approach for the impact of cross diffusion and gravity modulation on weakly nonlinear stability analysis of triple-diffusive Jeffrey fluid saturated in a porous media
  S. Sanju, Naveen Kumar R, Chandan K, R.S. Varun Kumar · Thermochimica Acta · Mar 6, 2026
• Adaptive physics-informed neural network-based digital twins integrated with Ensemble Kalman Filter
  Devavrat Thosar, Abhijit Bhakte, Zukui Li, Rajagopalan Srinivasan et al. · Computers & Chemical Engine... · Mar 3, 2026
• Physics-informed neural networks for groundwater: evidence, limits, and a roadmap
  Qingshan Ma, Qixin Gong, Weiya Ge, Miao Jing et al. · Environmental Earth Sciences · Feb 25, 2026

  Physics-informed neural networks (PINNs) have recently emerged as a promising machine learning paradigm that embeds physical laws into data-driven modeling, offering new opportunities for addressing long-standing challenges in groundwater s…

• Physics-Informed Neural Network for Quantifying Time-Encoded Arterial Spin Labeling: A Simulation Study
  Alessandro Giupponi, Chiara Da Villa, Mattia Veronese, Marco Castellaro · MIDL 2025 - Short Papers · May 1, 2025

  Arterial Spin Labeling (ASL) MRI enables non-invasive quantification of cerebral perfusion. Hadamard time-encoding improves acquisition efficiency and allows the simultaneous estimation of cerebral blood flow (CBF) and arterial transit time…

• Solving Kuramoto Oscillator Model using Physics Informed Neural Network
  MusIML Poster · Nov 30, 2024

  Physics informed machine learning has been emerged as a powerful tool with the help of deep learning as the latter has been instrumental as a data-driven function approximator. Many recent works have been focusing on solving hard to solve d…

• Pseudo Physics-Informed Neural Operators
  Keyan Chen, Yile Li, Da Long, WEI W. XING et al. · Submitted to ICLR 2025 · Sep 25, 2024

  Recent advancements in operator learning are transforming the landscape of computational physics and engineering, especially alongside the rapidly evolving field of physics-informed machine learning. The convergence of these areas offers ex…