Neural networks offer a flexible means to solve complex problems by learning from data. However, for scientific simulations that abide by certain physical laws, incorporating these principles directly into the learning process can lead to......
Transfer learning has become a cornerstone of modern machine learning, especially useful in domains where labeled data is scarce but meaningful features can be learned from related tasks. In scientific modeling, applying transfer learning......
Reaction-diffusion systems are mathematical models which correspond to various physical phenomena such as chemical reactions and biological pattern formation. Traditionally, simulating these systems involves numerically solving partial......
Inverse problems are a fascinating area of scientific computing that typically involve deducing unknown parameters or inputs of a mathematical model from observed/known outputs. A practical use of such problems can be found in fields......
PyTorch is a widely-used library for machine learning models due to its flexibility and the strong computational power it brings with GPU support. It is particularly useful in estimation and fitting problems in scientific domains, where......
The field of machine learning has gained significant traction in various domains, including complex simulations, due to its ability to approximate functions that are computationally expensive. One of the widely-used approaches for such......
Integrating physical constraints into neural networks can provide more accurate and realistic models, particularly in fields like engineering and physics where physical laws govern system behavior. This article explores how to implement......
Molecular dynamics (MD) simulations are a powerful tool used to study the physical movements of atoms and molecules in various scientific fields. By combining applied force fields and fundamental principles of physics, MD simulations......
The Finite Element Method (FEM) is a cornerstone in computational modeling and simulation, widely used in contexts ranging from structural analysis to fluid dynamics. However, FEM simulations often require intensive computation, especially......
Dynamic systems are omnipresent in various scientific fields ranging from physics to finance. They are used to model complex phenomena such as weather patterns, financial markets, and biological processes. In recent years, the use of......
Automatic differentiation is a powerful tool used in deep learning frameworks like PyTorch to compute gradients automatically and efficiently. This ability is essential for optimizing models, especially physics-based models where......
Partial Differential Equations (PDEs) are integral in describing various phenomena in scientific fields such as physics, engineering, and biology. Traditionally, numerical solvers like finite difference and finite element methods have been......