Multifidelity optimization can inform decision-making during process development and reduce the number of experiments ...

A machine-learning loop searched 14 million battery cathode compositions and found fivefold performance gains across four metrics using fewer than 200 experiments.

def problem(x): transl = 1/ np.sqrt(2) part1 = (x[0] - transl)**2 + (x[1] - transl)**2 part2 = (x[0] + transl)**2 + (x[1] + transl)**2 f1 = 1 - np.exp(-part1) f2 = 1 ...

uMOBO (Universal Multi-Objective Bayesian Optimization) is a specialized tool that gives AI Agents a "mathematical brain." While LLMs excel at reasoning and context, they struggle with ...

Abstract: This work presents a multi-objective Bayesian (MOB) optimization technique for co-optimizing device parameters and digital standard cell libraries (SDC) to deeply explore the technology ...

Abstract: We develop a new methodology to select scenarios of DER adoption most critical for distribution grids. Anticipating risks of future voltage and line flow violations due to additional PV ...

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...