:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chen, Nanxi, Cui, Chuanjie, Ma, Rujin, Chen, Airong, Wang, Sifan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computational Physics
Online Access:	https://arxiv.org/abs/2502.11942
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Enforcing hidden physics in physics-informed neural networks
by: Chen, Nanxi, et al.
Published: (2025)

Physics-informed neural operator for predictive parametric phase-field modelling
by: Chen, Nanxi, et al.
Published: (2026)

$Δ$-PINNs: physics-informed neural networks on complex geometries
by: Costabal, Francisco Sahli, et al.
Published: (2022)

Microseismic source imaging using physics-informed neural networks with hard constraints
by: Huang, Xinquan, et al.
Published: (2023)

Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective
by: Tucny, Jean-Michel, et al.
Published: (2025)

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
by: Agata, Ryoichiro, et al.
Published: (2026)

Self-adaptive weighting and sampling for physics-informed neural networks
by: Chen, Wenqian, et al.
Published: (2025)

Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
by: Straub, Christopher, et al.
Published: (2025)

Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics
by: Nair, Vineet Jagadeesan
Published: (2024)

Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
by: Zhuang, Bozhou, et al.
Published: (2024)

Training deep physical neural networks with local physical information bottleneck
by: Wang, Hao, et al.
Published: (2026)

PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks
by: Almanstötter, Marius, et al.
Published: (2025)

Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
by: Shahab, Muhammad Luthfi, et al.
Published: (2026)

Physics-informed neural networks for hidden boundary detection and flow field reconstruction
by: Zhu, Yongzheng, et al.
Published: (2025)

Efficient physics-informed neural networks using hash encoding
by: Huang, Xinquan, et al.
Published: (2023)

Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis
by: Li, Dong, et al.
Published: (2026)

Higher-order-ReLU-KANs (HRKANs) for solving physics-informed neural networks (PINNs) more accurately, robustly and faster
by: So, Chi Chiu, et al.
Published: (2024)

Optimal time sampling in physics-informed neural networks
by: Turinici, Gabriel
Published: (2024)

Aspects of holographic entanglement using physics-informed-neural-networks
by: Deb, Anirudh, et al.
Published: (2025)

Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers
by: Grontas, Panagiotis D., et al.
Published: (2025)

Efficient n-body simulations using physics informed graph neural networks
by: Ramos-Osuna, Víctor, et al.
Published: (2025)

Reconstructions of Jupiter's magnetic field using physics informed neural networks
by: Livermore, Philip W., et al.
Published: (2024)

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions
by: Wang, Sifan, et al.
Published: (2026)

Improving ideal MHD equilibrium accuracy with physics-informed neural networks
by: Thun, Timo, et al.
Published: (2025)

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble
by: Zou, Zongren, et al.
Published: (2025)

Distributed physics informed neural network for data-efficient solution to partial differential equations
by: Dwivedi, Vikas, et al.
Published: (2019)

Astral: training physics-informed neural networks with error majorants
by: Fanaskov, Vladimir, et al.
Published: (2024)

HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
by: Zheng, Haoyang, et al.
Published: (2023)

Improving physics-informed DeepONets with hard constraints
by: Brecht, Rüdiger, et al.
Published: (2023)

Automating modeling in mechanics: LLMs as designers of physics-constrained neural networks for constitutive modeling of materials
by: Tacke, Marius, et al.
Published: (2025)

Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks
by: Han, Shuai, et al.
Published: (2023)

Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
by: Shekarpaz, Simin, et al.
Published: (2023)

A general physics-constrained method for the modelling of equation's closure terms with sparse data
by: Chen, Tian, et al.
Published: (2025)

Physiological-model-based neural network for modeling the metabolic-heart rate relationship during physical activities
by: Zhang, Yaowen, et al.
Published: (2025)

Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves
by: Ehlers, Svenja, et al.
Published: (2025)

Convergence of physics-informed neural networks modeling time-harmonic wave fields
by: Schoder, Stefan, et al.
Published: (2025)

Towards agent-based-model informed neural networks
by: Antulov-Fantulin, Nino
Published: (2025)

Error estimation for physics-informed neural networks with implicit Runge-Kutta methods
by: Stiasny, Jochen, et al.
Published: (2024)

DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation
by: Cheng, Shijun, et al.
Published: (2025)

Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
by: Sedykh, Alexandr, et al.
Published: (2023)