1 citations · 1 across the 2 of their papers we have counts for
5 papers
Physics-Constrained Machine Learning for Chemical Engineering
Angan Mukherjee, Victor M. Zavala
Physics-constrained machine learning (PCML) combines physical models with data-driven approaches to improve reliability, generalizability, and interpretability. Although PCML has s…
On the Implementation of a Bayesian Optimization Framework for Interconnected Systems
Leonardo D. González, Victor M. Zavala
Bayesian optimization (BO) is an effective paradigm for the optimization of expensive-to-sample systems. Standard BO learns the performance of a system by using a Gaussian P…
Spatio-temporal load shifting for truly clean computing
Iegor Riepin, Tom Brown, Victor Zavala
Companies with datacenters are procuring significant amounts of renewable energy to reduce their carbon footprint. There is increasing interest in achieving 24/7 Carbon-Free Energy…
BOIS: Bayesian Optimization of Interconnected Systems
Leonardo D. González, Victor M. Zavala
Bayesian optimization (BO) has proven to be an effective paradigm for the global optimization of expensive-to-sample systems. One of the main advantages of BO is its use of Gaussia…
A Fast and Scalable Computational Topology Framework for the Euler Characteristic
Daniel J. Laky, Victor M. Zavala
The Euler characteristic (EC) is a powerful topological descriptor that can be used to quantify the shape of data objects that are represented as fields/manifolds. Fast methods for…