RiemannONets: Interpretable neural operators for Riemann problems
Develo** the proper representations for simulating high-speed flows with strong shock
waves, rarefactions, and contact discontinuities has been a long-standing question in …
waves, rarefactions, and contact discontinuities has been a long-standing question in …
Machine learning surrogate for 3D phase-field modeling of ferroelectric tip-induced electrical switching
Phase-field modeling offers a powerful tool for investigating the electrical control of the
domain structure in ferroelectrics. However, its broad application is constrained by …
domain structure in ferroelectrics. However, its broad application is constrained by …
[HTML][HTML] Transfer learning for accelerating phase-field modeling of ferroelectric domain formation in large-scale 3D systems
High-throughput phase-field simulations emerge as a compelling technique to predict the
evolution of domain structures in ferroelectric materials. Despite their potential, their …
evolution of domain structures in ferroelectric materials. Despite their potential, their …
Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators
Accurately predicting the long-term behavior of chaotic systems is crucial for various
applications such as climate modeling. However, achieving such predictions typically …
applications such as climate modeling. However, achieving such predictions typically …
Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone
Prolonged contact between a corrosive liquid and metal alloys can cause progressive
dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have …
dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have …
Reinforcement Learning-Assisted Ferroelectric Domain Wall Design Using a Machine Learning Phase-Field Surrogate
Precise manipulation of ferroelectric domain walls (DWs) has garnered increasing interest
for applications in DW memory devices. Although recent advancements in scanning probe …
for applications in DW memory devices. Although recent advancements in scanning probe …
Deep operator network surrogate for phase-field modeling of metal grain growth during solidification
A deep operator network (DeepONet) has been constructed that generates accurate
representations of phase-field model simulations for evolving two dimensional metal grain …
representations of phase-field model simulations for evolving two dimensional metal grain …
Benchmarking machine learning strategies for phase-field problems
We present a comprehensive benchmarking framework for evaluating machine-learning
approaches applied to phase-field problems. This framework focuses on four key analysis …
approaches applied to phase-field problems. This framework focuses on four key analysis …
LeapFrog: Getting the Jump on Multi-Scale Materials Simulations Using Machine Learning
The development of novel materials in recent years has been accelerated greatly by the use
of computational modelling techniques aimed at elucidating the complex physics controlling …
of computational modelling techniques aimed at elucidating the complex physics controlling …
Rethinking materials simulations: blending numerical simulations with various machine-learning strategies
R Dingreville - Modelling, Data Analytics and AI in Engineering … - madeai-eng.org
Materials simulations are omnipresent across diverse scientific domains including physical,
chemical, biological, and materials sciences. Existing state-of-the-art direct numerical …
chemical, biological, and materials sciences. Existing state-of-the-art direct numerical …