Neural operators for accelerating scientific simulations and design
Scientific discovery and engineering design are currently limited by the time and cost of
physical experiments. Numerical simulations are an alternative approach but are usually …
physical experiments. Numerical simulations are an alternative approach but are usually …
[HTML][HTML] Applications of artificial intelligence/machine learning to high-performance composites
With the booming prosperity of artificial intelligence (AI) technology, it triggers a paradigm
shift in engineering fields including material science. The integration of AI and machine …
shift in engineering fields including material science. The integration of AI and machine …
An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials
The purpose of this work is the development of a trained artificial neural network for
surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures …
surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures …
Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator
Long-term predictions of nonlinear dynamics of three-dimensional (3D) turbulence are very
challenging for machine learning approaches. In this paper, we propose an implicit U-Net …
challenging for machine learning approaches. In this paper, we propose an implicit U-Net …
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
We propose a novel finite element-based physics-informed operator learning framework that
allows for predicting spatiotemporal dynamics governed by partial differential equations …
allows for predicting spatiotemporal dynamics governed by partial differential equations …
Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains
Physics‐informed neural networks (PINNs) are a new tool for solving boundary value
problems by defining loss functions of neural networks based on governing equations …
problems by defining loss functions of neural networks based on governing equations …
3D elastic wave propagation with a factorized Fourier neural operator (F-FNO)
Numerical simulations are computationally demanding in three-dimensional (3D) settings
but they are often required to accurately represent physical phenomena. Neural operators …
but they are often required to accurately represent physical phenomena. Neural operators …
Physics‐informed neural operator solver and super‐resolution for solid mechanics
C Kaewnuratchadasorn, J Wang… - Computer‐Aided Civil …, 2024 - Wiley Online Library
Abstract Physics‐Informed Neural Networks (PINNs) have solved numerous mechanics
problems by training to minimize the loss functions of governing partial differential equations …
problems by training to minimize the loss functions of governing partial differential equations …
Accelerated multiscale mechanics modeling in a deep learning framework
Microstructural heterogeneity affects the macro-scale behavior of materials. Conversely,
load distribution at the macro-scale changes the microstructural response. These up-scaling …
load distribution at the macro-scale changes the microstructural response. These up-scaling …
Revealing the predictive power of neural operators for strain evolution in digital composites
The demand for high-performance materials, along with advanced synthesis technologies
such as additive manufacturing and 3D printing, has spurred the development of …
such as additive manufacturing and 3D printing, has spurred the development of …