Physics-Informed Neural Networks in Polymers: A Review DOI Open Access
Ivan Malashin, В С Тынченко, Andrei Gantimurov

et al.

Polymers, Journal Year: 2025, Volume and Issue: 17(8), P. 1108 - 1108

Published: April 19, 2025

The modeling and simulation of polymer systems present unique challenges due to their intrinsic complexity multi-scale behavior. Traditional computational methods, while effective, often struggle balance accuracy with efficiency, especially when bridging the atomistic macroscopic scales. Recently, physics-informed neural networks (PINNs) have emerged as a promising tool that integrates data-driven learning governing physical laws system. This review discusses development application PINNs in context science. It summarizes recent advances, outlines key methodologies, analyzes benefits limitations using for property prediction, structural design, process optimization. Finally, it identifies current future research directions further leverage advanced modeling.

Language: Английский

Integrating neural operators with diffusion models improves spectral representation in turbulence modelling DOI
Vivek Oommen, Aniruddha Bora, Zhen Zhang

et al.

Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, Journal Year: 2025, Volume and Issue: 481(2309)

Published: March 1, 2025

We integrate neural operators with diffusion models to address the spectral limitations of in surrogate modelling turbulent flows. While offer computational efficiency, they exhibit deficiencies capturing high-frequency flow dynamics, resulting overly smooth approximations. To overcome this, we condition on enhance resolution structures. Our approach is validated for different diverse datasets, including a high-Reynolds-number jet-flow simulation and experimental Schlieren velocimetry. The proposed method significantly improves alignment predicted energy spectra true distributions compared alone. This enables stabilize longer forecasts through diffusion-corrected autoregressive (AR) rollouts, as demonstrate this work. In addition, proper orthogonal decomposition (POD) analysis demonstrates enhanced fidelity space–time. work establishes new paradigm combining generative advance systems, it can be used other scientific applications that involve microstructure content. See our project page: vivekoommen.github.io/NO_DM .

Language: Английский

Citations

1
Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification DOI
Zongren Zou, George Em Karniadakis

Journal of Computational Physics, Journal Year: 2025, Volume and Issue: unknown, P. 113947 - 113947

Published: March 1, 2025

Language: Английский

Citations

0
A physics-informed neural network method for thermal analysis in laser-irradiated 3D skin tissues with embedded vasculature, tumor and gold nanorods DOI
Farnaz Rezaei, Weizhong Dai, Shayan Davani

et al.

International Journal of Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 245, P. 126980 - 126980

Published: March 30, 2025

Language: Английский

Citations

0
Physics-based machine learning for computational fracture mechanics DOI Creative Commons
Fadi Aldakheel,

Elsayed S. Elsayed,

Yousef Heider

et al.

Machine learning for computational science and engineering, Journal Year: 2025, Volume and Issue: 1(1)

Published: April 16, 2025

Abstract This study introduces a physics-based machine learning ( $$\phi $$ ϕ ML) framework for modeling both brittle and ductile fractures in elastic-viscoplastic materials. It integrates physical principles, including governing equations constraints, directly into the neural network architecture. Specifically, feedforward is designed to embed laws within its architecture, ensuring thermodynamic consistency. Building on this foundation, synthetic datasets generated from finite element-based phase-field fracture simulations are employed train proposed framework, focusing capturing homogeneous, one-dimensional responses. Detailed analyses performed stored elastic energy dissipated work due plasticity fracture, demonstrating capability of predict essential features. The ML overcomes shortcomings classical models, which rely heavily large lack guarantees principles. By leveraging physics-integrated design, demonstrates exceptional performance predicting key properties with limited training data. ensures reliability, efficiency, consistency, establishing foundational approach integrating computational mechanics.

Language: Английский

Citations

0
Physics-Informed Neural Networks in Polymers: A Review DOI Open Access
Ivan Malashin, В С Тынченко, Andrei Gantimurov

et al.

Polymers, Journal Year: 2025, Volume and Issue: 17(8), P. 1108 - 1108

Published: April 19, 2025

The modeling and simulation of polymer systems present unique challenges due to their intrinsic complexity multi-scale behavior. Traditional computational methods, while effective, often struggle balance accuracy with efficiency, especially when bridging the atomistic macroscopic scales. Recently, physics-informed neural networks (PINNs) have emerged as a promising tool that integrates data-driven learning governing physical laws system. This review discusses development application PINNs in context science. It summarizes recent advances, outlines key methodologies, analyzes benefits limitations using for property prediction, structural design, process optimization. Finally, it identifies current future research directions further leverage advanced modeling.

Language: Английский

Citations

0