Latest Physics-Informed Neural Nets Research Papers
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- Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for PortuguesePedro Carvalho Brom · arXiv · Oct 29, 2026
Building a language model of one's own no longer requires a data centre. This tutorial trains one from scratch, tokenizer included, on one consumer GPU, and shows how to tell whether it is good for the tasks of a specialist domain. It propo…
- IMPACT: Inverter-Informed Multimodal Learning for Photovoltaic Generation ForecastingAnushka Katiyar, Cong Feng, Jing Wang, Tristan Liu et al. · arXiv · Oct 29, 2026
This work develops a physics-informed multimodal deep learning framework for 10- to 110-minute-ahead probabilistic photovoltaic power forecasting. The forecasting model is built on a four-branch architecture that separately encodes plant-le…
- A physics-informed neural network-based computational method for forward and inverse analysis of buried pipelines subjected to lateral landslidesZhou Xinxing, Wang Fuping, Li Liangliang, Liu Tianping et al. · Frontiers in Mechanical Eng... · Oct 28, 2026
Buried oil and gas pipelines in mountainous regions are frequently subjected to transverse landslide loading, necessitating accurate forward mechanical prediction and reliable inverse load identification for structural safety evaluation. Co…
- Accuracy and Computational Cost of Standard and Sequential Time-Window Physics-Informed Neural Networks for Transient Heat Conduction with a Moving Gaussian Heat SourceSyukur Rahmat Gulo, Rian Hidayatillah, Kasih Indah Zebua, Susi Komalasari et al. · F1000Research · Oct 28, 2026
Background The thermal field generated by a moving laser heat source evolves continuously as the laser scans across a material surface. Physics-Informed Neural Networks (PINNs) offer an alternative for modeling transient heat transfer, but …
- Flame response inference from pressure-only data via physics-informed neural networksSurya Vamshi Penumarthi, Korbinian Niebler, Camilo F. Silva · International Journal of Sp... · Oct 28, 2026
This study presents a physics-informed neural network framework to reconstruct spatio-temporal acoustic pressure fields and hidden quantities—namely, velocity and heat-release-rate fluctuations—in axial combustors using only sparse pressure…
- Physics-Informed Global Model Development via Federated Learning for Indoor Temperature Prediction Toward Advanced Building ControlAbu Talib, Kwangwon Choi, Jaewan Joe · Architectural research · Oct 28, 2026
Model-based predictive control provides an effective approach for improving building energy efficiency by optimizing heating, ventilation, and air-conditioning operation while maintaining indoor thermal conditions. However, its performance …
- Physics-Informed Modeling of Transient Pulsating Microchannel Flow with Heat-Transfer and Entropy-Generation Trade-Off AnalysisAswin Karkadakattil · International Journal of Th... · Oct 28, 2026
Pulsating flow offers a practical means of modifying convective heat transfer in microchannels, but thermal enhancement may be accompanied by increased thermodynamic irreversibility. This study develops a physics-informed neural network (PI…
- QD-PINN:A Physics-Informed Neural Network Approach to Bayesian Quickest Detection in High-Dimensional DiffusionsHang Ye, Yi Liu, Haipeng Xing · arXiv · Oct 27, 2026
Quickest detection problems (QDPs) arise in a wide range of applications. However, the complementarity structure of the associated variational inequalities poses substantial challenges for traditional numerical methods, particularly in high…
- Integrated MCNP Reliability Diagnosis Framework using Data-Driven Pattern Analysis and Physics-Informed Neural NetworkJin-Ho Jeong, Hyunsoo Lee · Journal of Korean institute... · Oct 27, 2026
본 연구는 Monte Carlo N-Particle(MCNP) 시뮬레이션의 수렴성 판정 시 발생하는 판정 일관성 부족과 통계적 불확실성을 해소하기 위해, 데이터 주도형 분석과 물리 정보 신경망(PINN)을 결합한 통합 진단 프레임워크를 제안한다. 이를 위해 LSTM과 ResNet을 활용하여 MCNP의 시계열 및 정적 통계 지표를 분석하였으며, ResNet의 은닉 레이어 수와 활성화 함수에 대한 그리드 탐색(4×4=16 조합)결…
- A hierarchical adaptive Fourier-feature physics-informed neural network for radio-frequency capacitively coupled plasma dischargesTong Wu, Jingyu Li, Wen-Zhu Jia, Qian He et al. · Machine Learning Science an... · Oct 27, 2026
Abstract Physics-informed neural networks (PINNs) are difficult to train for radio-frequency capacitively coupled plasma (RF-CCP) fluid--Poisson systems because missing initial conditions, steep sheath gradients, and strongly imbalanced res…
- Next-Generation Zero-Server Edge Electronic Design Automation (Edge Web EDA): Circuit Simulation Architecture via V8 JIT Dynamic Compilation, WebGPU Mixed-Precision Sparse Matrix Solving, and Physics-Informed Neural NetworksI-Wei Tseng · Zenodo (CERN European Organ... · Oct 26, 2026
- Water-Balance Physics-Informed Neural Network for Daily Streamflow Prediction: A Single-Catchment Comparative Study Using CAMELS-GB v2Ghazi Mauer Idroes, Nazrul Effendy, Fatimah Zaharany Annissa · International Journal of Hy... · Oct 26, 2026
Accurate daily streamflow prediction remains challenging because rainfall–runoff dynamics are nonlinear, temporally dependent, and subject to water-mass conservation. This study examines whether a simplified catchment water-balance constrai…
- Target-Guided Selective Reweighting for physics-informed neural network inverse problems: A transfer learning approachQian Hu, Bin Fan, Y H Xiao, Zhicheng Lin et al. · Engineering Analysis with B... · Oct 26, 2026
- Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-informed Generative NetworksAshutosh Kumar Mishra, Emma Elizabeth Tolley, Nicolas Cerardi · The Astrophysical Journal · Oct 26, 2026
Abstract Generative machine learning models have recently emerged as powerful tools for producing cosmological simulations. However, many existing emulators do not explicitly enforce the underlying physical dynamics governing cosmological e…
- A provenance-aware zirconia conductivity database and Arrhenius-constrained screening of doped electrolyte candidatesQikai Zhang, Zhihao Jin, Xianfu Chen, Hao Xiong et al. · Machine Learning Science an... · Oct 26, 2026
Abstract Zirconia solid electrolytes are key oxygen-ion conductors, but reported conductivity values depend strongly on dopant chemistry, processing history and measurement temperature. Here we curate a provenance-aware zirconia conductivit…
- PINN framework with bilinear modelTewele Welu · Zenodo (CERN European Organ... · Oct 25, 2026
Python/JAX code accompanying the article on a four-compartment bilinear tumor–immune–drug model with tumor-induced immunosuppression. It reproduces the equilibrium and reproduction-number analysis, the transcritical bifurcation quantities, …
- Dynamic Prediction of Defective Composite Shells via Physics-Informed Data-Fusion TransformerJian Zang, Yu-Hang Ke, Duo Xu, Ye-Wei Zhang et al. · AIAA Journal · Oct 25, 2026
Composite open-shell structures manufactured using low-cost wet layup processes in electric aircraft are prone to resin unevenness and interlayer voids, resulting in multiple manufacturing defects. These defects induce localized stiffness d…
- Reconstruction of pulsatile flow in curved pipes using physics-informed neural networksXingchao Zhang, Fan Wu, Yunfan Yang, Hongping Wang et al. · European Journal of Mechani... · Oct 24, 2026
- Physics-Informed and Data-Driven Neural Networks for Modeling and Optimization of Residual Stresses in Dissimilar Material SystemsAnkit Tyagi, Abhishek Dadhich, Ankita Dadhich, Sachin Sirohi et al. · Journal of Materials Engine... · Oct 24, 2026
- Physics-informed machine learning framework for predictive maintenance and multi-fault classification in centrifugal pumpsMayank Srivastava, Himanshu Kumar, Ankit Bansal, Anil Kumar · Sadhana · Oct 24, 2026
- Physics-informed neural networks for downscaling convective-permitting rainfall extremes under sparse radiosonde initialization: a climate-analytics evaluation of tail-index distortion in dam-safety analyticsAkosua Serwaa Amankwah, Efua Abena Owusu-Boateng, Kojo Nkrumah-Bonsu · Zenodo (CERN European Organ... · Oct 24, 2026
Physics-informed neural networks (PINNs) are increasingly proposed for downscaling convective-permitting rainfall extremes, yet their behaviour under sparse radiosonde initialization remains poorly characterised for dam-safety analytics, wh…
- ZENO: Nonlinear filtering with Zakai Equation Neural OperatorsAaditya Prasad, Steven W. Flavell, Brian DePasquale · arXiv · Oct 23, 2026
State inference and parameter learning for nonlinear filters is a core computation for scientific machine learning with varied applications in engineering and neuroscience. The unnormalized filtering distribution is governed by the Zakai eq…
- Fe₃O₄–CNT/paraffin nanoparticles impact on MHD Darcy–Forchheimer flow heat transfer using physics-informed neural networkAbdul Waheed, Hong Qi, Ilyas Khan, A.R. Khan · Materials Today Communications · Oct 23, 2026
- Discontinuous Galerkin Finite Element Operator Network for Solving Non-Smooth PDEsKapil Chawla, Youngjoon Hong, Jae Yong Lee, Sanghyun Lee · SIAM Journal on Scientific ... · Oct 23, 2026
Abstract. We introduce the Discontinuous Galerkin Finite Element Operator Network (DG–FEONet), a data-free operator learning framework that combines the strengths of the discontinuous Galerkin (DG) method with neural networks to solve param…
- Real-time state-of-health estimation and online degradation prognosis from partial battery discharge using Physics-Informed Neural NetworksBegoña Ispizua, Serio Gil-López, Leire Arrizabalaga, Ibai Laña · Journal of Power Sources · Oct 23, 2026
With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degradation behavior critical. In this work, we propose a physics-i…
- Physics-guided non-crossing probabilistic forecasting for distributed photovoltaic clusters using dynamic graph attentionZhihai Yan, Tianyu Xu, Zhongkai Shen, Wei Wei et al. · Frontiers in Energy Research · Oct 23, 2026
Accurate probabilistic forecasting of geographically distributed photovoltaic (PV) generation is challenged by incomplete measurements, weather-dependent cross-site correlations, and quantile crossing. This study proposes a physics-guided f…
- Hybrid AI-Mathematical Modelling for Nonlinear Dynamical SystemsHarjit Kumar, Prabhat Kumar, Vinod Kumar, John Abhishek Masih et al. · Mathematics for Applications · Oct 23, 2026
Nonlinear dynamical systems underpin phenomena across fluid mechanics, epidemiology, climate science, and engineering, yet their inherent complexity renders classical analytical methods insufficient for real-world applications. This paper p…
- Online hydrodynamic parameter identification for an ROV using an enhanced physics-informed neural networkWeidong Jiang, Yi‐Ting Wang, Lei Wang, Ming Ding · Ocean Engineering · Oct 23, 2026
Remotely operated vehicles (ROVs) require accurate hydrodynamic parameters for reliable model-based control. Online identification remains difficult because measurements are noisy, parameters can differ by several orders of magnitude, and w…
- PI-STGNN model-driven risk assessment method for leapfrog tripping in photovoltaic power plantsWenjiu Xu, Jinlong Wang, Lei Wang, Jun Zhou et al. · Frontiers in Energy Research · Oct 22, 2026
Unintended fault propagation in photovoltaic plants stems primarily from collector-line leapfrog tripping, a hazard characterized by multi-factor coupling, time-varying dynamics and hidden causal links that remain difficult to fully charact…
- A hybrid physics-informed machine learning and control framework for predictive modeling in additive manufacturingTariku Sinshaw Tamir, Abul Fazal Muhammad Arif, Syed Sohail Akhtar, Jiewu Leng et al. · Journal of Intelligent Manu... · Oct 22, 2026