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

In recent years, the integration of intelligent industrial process monitoring, quality prediction,
and predictive maintenance solutions has garnered significant attention, driven by rapid …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

This paper proposes, implements, and evaluates a reinforcement learning (RL)-based
computational framework for automatic mesh generation. Mesh generation plays a …

Finite element discretizations of problems in computational physics often rely on adaptive
mesh refinement (AMR) to preferentially resolve regions containing important features …

Abstract The Finite Element Method, an important technique in engineering, is aided by
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …

Abstract Numerical Partial Differential Equation (PDE) solvers often require discretizing the
physical domain by using a mesh. Mesh movement methods provide the capability to …

This work presents a novel approach capable of predicting an appropriate spacing function
that can be used to generate a near-optimal mesh suitable for simulation. The main …

In this work, we revisit the marking decisions made in the standard adaptive finite element
method (AFEM). Experience shows that a naïve marking policy leads to inefficient use of …

Solving partial differential equations (PDEs) is the core of many fields of science and
engineering. While classical approaches are often prohibitively slow, machine learning …