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 …

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 …

Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

Physics-informed neural networks (PINN) can solve partial differential equations (PDEs) by
encoding the mathematical information explicitly into the loss functions. In the context of …

The paper presents an efficient and robust data-driven deep learning (DL) computational
framework developed for linear continuum elasticity problems. The methodology is based on …

Abstract Machine learning (ML) has evolved as a technology used in even broader domains,
ranging from spam detection to space exploration, as a result of the boom in available data …

Current work presents an efficient data-driven Physics Informed Neural Networks (PINNs)
computational framework for the solution of elastoplastic solid mechanics. To incorporate …

Driven by the need to accelerate numerical simulations, the use of machine learning
techniques is rapidly growing in the field of computational solid mechanics. Their application …

In the present work, a physics-informed deep learning-based constitutive modeling
approach has been introduced, for the first time, to solve non-associative Drucker–Prager …

Forming processes are known for their intricacies in prediction and control due to the
complex loading conditions and material flow. This paper will first introduce the AI algorithms …