Computational Physic

Computational physics leverages advanced computational methods, increasingly incorporating machine learning, to solve complex physical problems described by partial differential equations (PDEs). Current research focuses on developing stable and efficient deep learning architectures, such as physics-informed neural networks (PINNs) and novel residual networks, to improve the accuracy and speed of PDE solvers, while also addressing issues like weak baselines and reporting biases in evaluating these methods. This interdisciplinary field is crucial for accelerating scientific discovery and technological advancement across diverse areas, from materials science and fluid dynamics to climate modeling and astrophysics, by enabling more efficient and accurate simulations of complex physical phenomena.