We introduce a simple, rigorous, and unified framework for solving nonlinear partial
differential equations (PDEs), and for solving inverse problems (IPs) involving the …

Boundary value problems (BVPs) play a central role in the mathematical analysis of
constrained physical systems subjected to external forces. Consequently, BVPs frequently …

Reduced order modeling is an important and fast-growing research field in computational
science and engineering, motivated by several reasons, of which we mention just a few …

A computational framework is proposed for efficiently solving multidisciplinary analysis and
optimization (MDAO) problems in a relatively high-dimensional design parameter space. It …

Nowadays, the shipbuilding industry is facing a radical change toward solutions with a
smaller environmental impact. This can be achieved with low emissions engines, optimized …

Emulator embedded neural networks, which are closely related to physics informed neural
networks, leverage multi-fidelity data sources for efficient design exploration of aerospace …

This thesis introduces a new framework for solving optimization problems efficiently in
higher-dimensional parameter spaces constrained by partial differential equations (PDEs) …

This thesis is about numerical methods for scientific computing and scientific machine
learning, with a focus on solving partial differential equations (PDEs) and inverse problems …

Abstract The design of Hypersonic Vehicles (HVs) requires meeting multiple unconventional
and often conflicting design requirements in a hostile, high-energy environment. The most …

Aeroelastic flutter is a dynamically complex phenomenon that has adverse and unstable
effects on elastic structures. It is crucial to better predict the phenomenon of flutter within the …