Hugging Face Trending Papers

Physics-Informed Neural Network Modeling of Biodegradable Contaminant Transport through GCL/SL Composite Liners

This study develops a two-domain physics-informed neural network framework for contaminant transport through a GCL/SL composite liner system, in which the thin GCL layer is treated using a steady-state advection-dispersion-biodegradation formulation and the underlying soil liner is modeled as a transient transport domain. Two formulations are evaluated against analytical and finite-element reference solutions under different leachate-head conditions: a standard PINN with soft constraint enforcement (Std-PINN) and a hard-constrained PINN (H-PINN), in which selected boundary and initial conditions are embedded directly into the trial solutions.

arXiv Machine Learning

Jun 4

Physics-Informed Neural Network Modeling of Biodegradable Contaminant Transport through GCL/SL Composite Liners

arXiv:2606. 04392v1 Announce Type: new Abstract: This study develops a two-domain physics-informed neural network framework for contaminant transport through a GCL/SL composite liner system, in which the thin GCL layer is treated using a steady-state advection-dispersion-biodegradation formulation and the underlying soil liner is modeled as a transient transport domain.

By Dong Li, Yapeng Cao, Haiping Zhao, Shutong Han

arXiv Machine Learning

Jun 9

A Machine Learning-Enhanced Hopf-Cole Formulation for Nonlinear Gas Flow in Porous Media

arXiv:2603. 11250v2 Announce Type: replace-cross Abstract: Accurate modeling of gas flow through porous media is critical for many technological applications, including reservoir performance prediction, carbon capture and sequestration, and fuel cells and batteries.

By V. S. Maduri, K. B. Nakshatrala

arXiv Machine Learning

Jul 7

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach

arXiv:2607. 05271v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) encounter ill-posed optimization, loss competition, and parameter compensation in partial differential equation (PDE) inverse problems.

By Qian Hu, Bin Fan, Yao Xiao, Zhicheng Lin, Meixin Xiong

diffusion fine-tuning efficiency benchmarks

arXiv Machine Learning

Aug 4

A Physics-Chemistry-Informed Neural Network (PCINN) for Real-Time Spatial-ALD Coverage Prediction and Reliable Kinetics Inversion

arXiv:2608. 00212v1 Announce Type: new Abstract: Spatial atomic layer deposition (SALD) is a leading atmospheric-pressure, high-throughput route to industrial ALD, but design and control are limited by the cost of predicting surface coverage: high-fidelity CFD is far too slow for operating-window scans, while analytic models miss transport modulation such as the gas curtain.

By Ning Hu, Chang Liu, Yunlei Jiang, Yuan Dong

efficiency

arXiv AI

Jun 6

Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data

arXiv:2606. 05199v1 Announce Type: cross Abstract: The identification of constitutive neural network models from heterogeneous full-field deformation data provides a robust alternative to traditional calibration methods based on homogeneous stress-strain experiments, particularly given the high dimensionality of trainable parameters.

By Matthias Knipper, Chenyi Ji, Malte Brand, Kevin Linka

arXiv Machine Learning

Jul 15

A hybrid analytical-PINN model for subsurface simulation of geothermal heat exchangers in heterogeneous underground

arXiv:2607. 12271v1 Announce Type: new Abstract: In this paper, a parametric physics-informed neural network for solving the heterogeneous soil thermal problem with borehole heat exchangers (BHEs) as singular sources is developed.

By Moke Rao, Thomas Hamacher, Smajil Halilovic

diffusion

arXiv Machine Learning

Jun 29

Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte

arXiv:2606. 28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models.

By Gift Modekwe, Qiugang Lu

diffusion fine-tuning

arXiv Machine Learning

Jun 3

Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative

arXiv:2606. 03210v1 Announce Type: cross Abstract: Automatic-differentiation-based inverse analysis methods, including physics-informed neural networks (PINNs) and differentiable programming, have recently shown great promise due to their ability to compute accurate gradients and convergence efficiency.

By Yongjin Choi, Hyeonbin Moon, Seunghwa Ryu

benchmarks

arXiv AI

Jun 30

Hard-constraint physics-residual networks for hydrogen crossover prediction and high-pressure extrapolation in PEM water electrolysis

arXiv:2511. 05879v5 Announce Type: replace-cross Abstract: Hydrogen crossover is a critical safety and efficiency constraint in high-pressure polymer electrolyte membrane water electrolysis (PEMWE), but accurate prediction remains difficult because data are limited, transport physics are strongly coupled, and industrial operation requires reliable extrapolation beyond observed conditions.

By Yong-Woon Kim, Jihyeok Lee, Chulung Kang, Yung-Cheol Byun

diffusion benchmarks safety

arXiv Machine Learning

Jul 20

A Physics-Informed Neural Network with a Modified Lorentzian Activation for Nonlocal Gradient-Flow Equations in Dynamic Density Functional Theory

arXiv:2607. 15291v1 Announce Type: cross Abstract: We develop a physics-informed neural network (PINN) framework for nonlocal partial differential equations arising in dynamic density functional theory (DDFT).

By Dimitrios Gourzoulidis, Soumaya Elkantassi, Serafim Kalliadasis

arXiv AI

Jun 15

Korzhinskii-Net: Physics-Informed Neural Network for Sub-Surface Mineral Prospectivity Modelling

arXiv:2606. 13695v1 Announce Type: cross Abstract: Mineral prospectivity modelling (MPM) underpins exploration economics, yet most operational pipelines reduce to data-driven classifiers trained on shallow surface proxies.

By Boris Kriuk

arXiv AI

Jun 2

Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects

arXiv:2606. 00056v1 Announce Type: cross Abstract: This study develops a dimensionless multi-domain physics-informed neural network (PINN) framework for electro-osmotic radial consolidation considering smear effects and combined vacuum and surcharge loading.

By Dong Li, Yapeng Cao, Shuai Huang, Yujun Cui, Haiping Fu, Lu Yang, He Wei

rag

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