Over the past decades, classical optimization methods, including gradient-based topology
optimization and the evolutionary algorithm, have been widely employed for the inverse …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

This paper explores the recent advancements in enhancing Computational Fluid Dynamics
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …

In this work we investigate the use of deep inverse models (DIMs) for designing artificial
electromagnetic materials (AEMs)–such as metamaterials, photonic crystals, and …

We consider the task of solving generic inverse problems, where one wishes to determine
the hidden parameters of a natural system that will give rise to a particular set of …

There have been significant advances in simulation models predicting human behavior
across various interactive tasks. One issue remains, however: identifying the parameter …

In this paper, we analyze the properties of invertible neural networks, which provide a way of
solving inverse problems. Our main focus lies on investigating and controlling the Lipschitz …

The ability to readily design novel materials with chosen functional properties on-demand
represents a next frontier in materials discovery. However, thoroughly and efficiently …

Due to the remarkable ability of Deep learning (DL) to abstract hidden information, it has
been proven to be a powerful tool in many tasks related to the design of metamaterials. DL …

Many machine learning tasks in neuroimaging aim at modeling complex relationships
between a brain's morphology as seen in structural MR images and clinical scores and …