Process-Based Modeling (PBM) and Machine Learning (ML) are often perceiv...
Numerical modeling and simulation have become indispensable tools for
ad...
In this paper, we present a physics-constrained deep neural network (PCD...
Partial differential equations (PDEs) are fundamental for theoretically
...
Uncertainty quantification for forward and inverse problems is a central...
We investigate the use of discrete and continuous versions of
physics-in...
In this work, we propose a new Gaussian process regression (GPR)-based
m...
In this work, we propose a new Gaussian process regression (GPR) method:...