The curse-of-dimensionality (CoD) taxes computational resources heavily ...
High order schemes are known to be unstable in the presence of shock
dis...
The molten sand, a mixture of calcia, magnesia, alumina, and silicate, k...
Scientific Machine Learning (SciML) has advanced recently across many
di...
We propose a framework and an algorithm to uncover the unknown parts of
...
Numerical simulation for climate modeling resolving all important scales...
Deep neural operators, such as DeepONets, have changed the paradigm in
h...
We compare high-order methods including spectral difference (SD), flux
r...
Physics-informed machine learning (PIML) has emerged as a promising new
...
Phase-field modeling is an effective mesoscale method for capturing the
...
We develop a distributed framework for the physics-informed neural netwo...
Wave propagation in real media is affected by various non-trivial physic...
We introduce an optimized physics-informed neural network (PINN) trained...