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 …

For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …

The curse-of-dimensionality taxes computational resources heavily with exponentially
increasing computational cost as the dimension increases. This poses great challenges in …

Recent work in scientific machine learning (SciML) has focused on incorporating partial
differential equation (PDE) information into the learning process. Much of this work has …

Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …

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 …

We utilize neural operators to learn the solution propagator for challenging systems of
differential equations that are representative of stiff chemical kinetics. Specifically, we apply …

Operator learning for complex nonlinear systems is increasingly common in modeling multi-
physics and multi-scale systems. However, training such high-dimensional operators …

Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …

Neural operator learning models have emerged as very effective surrogates in data-driven
methods for partial differential equations (PDEs) across different applications from …