We develop a methodology to create data-driven predictive digital twins ...
We present a novel method for learning reduced-order models of dynamical...
We present a novel framework for learning cost-efficient latent
represen...
Lattice-like structures can provide a combination of high stiffness with...
This paper proposes a novel approach for learning a data-driven quadrati...
We present a parsimonious surrogate framework for learning high dimensio...
We present a new scientific machine learning method that learns from dat...
Reliable, risk-averse design of complex engineering systems with optimiz...
This paper derives predictive reduced-order models for rocket engine
com...
This work presents a non-intrusive model reduction method to learn
low-d...
We present Lift Learn, a physics-informed method for learning
low-di...
This paper presents a physics-based data-driven method to learn predicti...
This paper develops a multifidelity method that enables estimation of fa...
This paper presents a structure-exploiting nonlinear model reduction met...
In many situations across computational science and engineering, multipl...