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 …

Evolutionary computation is a rapidly evolving field and the related algorithms have been
successfully used to solve various real-world optimization problems. The past decade has …

DOLFINx is the next generation problem solving environment from the FEniCS Project; it
provides an expressive and performant environment for solving partial differential equations …

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

Repeatedly solving nonlinear partial differential equations with varying parameters is often
an essential requirement to characterise the parametric dependences of dynamical systems …

In numerous engineering projects, such as tunnel construction, underground gas storage in
caverns, and the impact of earthquakes, rock materials experience cyclic loading. However …

This paper presents a general Convolution Hierarchical Deep-learning Neural Networks (C-
HiDeNN) computational framework for solving partial differential equations. This is the first …

Landslides are widely acknowledged as among the most prevalent natural disasters.
Peridynamics (PD), a mesh-free computational method, offers distinctive advantages in …

Numerical modeling tools are essential in aircraft structural design, yet they face challenges
in accurately reflecting real-world behavior due to factors like material properties scatter and …

Recent advancements in inverse design approaches, exemplified by their large-scale
optimization of all geometrical degrees of freedom, have provided a significant paradigm …