:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Dong, Huang, Shuai, Cao, Yapeng, Cui, Yujun, Wei, Xiaobin, Cao, Hongtao
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Computational Physics
Online Access:	https://arxiv.org/abs/2602.07031
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects
by: Li, Dong, et al.
Published: (2026)

Towards physics-informed neural networks for landslide prediction
by: Dahal, Ashok, et al.
Published: (2024)

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

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

Graph neural networks informed locally by thermodynamics
by: Tierz, Alicia, et al.
Published: (2024)

Binary structured physics-informed neural networks for solving equations with rapidly changing solutions
by: Liu, Yanzhi, et al.
Published: (2024)

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

Grey-informed neural network for time-series forecasting
by: Xie, Wanli, et al.
Published: (2024)

Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks
by: Chen, Wenqian, 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)

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

Deep-layer limit and stability analysis of the basic forward-backward-splitting induced network (II): learning problems
by: Lin, Xuan, et al.
Published: (2026)

On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach
by: Yeregui, Josu, 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)

Logic-informed reinforcement learning for cross-domain optimization of large-scale cyber-physical systems
by: Wan, Guangxi, et al.
Published: (2025)

Kolmogorov-Arnold graph neural networks for chemically informed prediction tasks on inorganic nanomaterials
by: Volzhin, Nikita, et al.
Published: (2025)

IncepFormerNet: A multi-scale multi-head attention network for SSVEP classification
by: Huang, Yan, et al.
Published: (2025)

Data-driven building energy efficiency prediction using physics-informed neural networks
by: Michalakopoulos, Vasilis, et al.
Published: (2023)

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

Interpolating neural network: A novel unification of machine learning and interpolation theory
by: Park, Chanwook, et al.
Published: (2024)

Can LLMs Serve As Time Series Anomaly Detectors?
by: Dong, Manqing, et al.
Published: (2024)

Epistemically-guided forward-backward exploration
by: Urpí, Núria Armengol, et al.
Published: (2025)

Improving physics-informed neural network extrapolation via transfer learning and adaptive activation functions
by: Papastathopoulos-Katsaros, Athanasios, et al.
Published: (2025)

A comparative analysis of a neural network with calculated weights and a neural network with random generation of weights based on the training dataset size
by: Geidarov, Polad
Published: (2025)

Conformal mapping based Physics-informed neural networks for designing neutral inclusions
by: Cho, Daehee, et al.
Published: (2025)

Holistic analysis on the sustainability of Federated Learning across AI product lifecycle
by: Cao, Hongliu
Published: (2023)

Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networks
by: Mircea, Maria, et al.
Published: (2024)

On permutation-invariant neural networks
by: Kimura, Masanari, et al.
Published: (2024)

Sobolev acceleration for neural networks
by: Oh, Jong Kwon, et al.
Published: (2025)

Attention mechanisms in neural networks
by: Hays, Hasi
Published: (2026)

Preferential subspace identification (PSID) with forward-backward smoothing
by: Sani, Omid G., et al.
Published: (2025)

Principles of Lipschitz continuity in neural networks
by: Luo, Róisín
Published: (2026)

Linearity-based neural network compression
by: Dobler, Silas, et al.
Published: (2025)

MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting
by: Wu, Binqing, et al.
Published: (2025)

Detecting hidden structures from a static loading experiment: topology optimization meets physics-informed neural networks
by: Mowlavi, Saviz, et al.
Published: (2023)

Applying graph neural network to SupplyGraph for supply chain network
by: Han, Kihwan
Published: (2024)

M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection
by: Huang, Chao, et al.
Published: (2026)

EMA Without the Lag: Bias-Corrected Iterate Averaging Schemes
by: Block, Adam, et al.
Published: (2025)

Understanding the dynamics of the frequency bias in neural networks
by: Molina, Juan, et al.
Published: (2024)

Graph neural networks and non-commuting operators
by: Velasco, Mauricio, et al.
Published: (2024)