70 citations · 150 across the 31 of their papers we have counts for
35 papers
Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap
Rafael Ferreira da Silva, Milad Abolhasani, Peter Beaucage +25
One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The fi…
ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
Weimin Huang, Natalie M. Isenberg, Ján Drgoňa +2
Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial optimization problems is ch…
Efficient Gradient-Based Optimization for Joint Layout Design and Control of Wind Turbines
James Kotary, Natalie Isenberg, Draguna Vrabie
A central challenge in the design of energy-efficient wind farms is the presence of wake effects between turbines. When a wind turbine harvests energy from free wind, it produces a…
Homotopy-Guided Self-Supervised Learning of Parametric Solutions for AC Optimal Power Flow
Shimiao Li, Aaron Tuor, Draguna Vrabie +2
Learning to optimize (L2O) parametric approximations of AC optimal power flow (AC-OPF) solutions offers the potential for fast, reusable decision-making in real-time power system o…
Learning to Solve Constrained Bilevel Control Co-Design Problems
James Kotary, Himanshu Sharma, Ethan King +3
Learning to Optimize (L2O) is a subfield of machine learning (ML) in which ML models are trained to solve parametric optimization problems. The general goal is to learn a fast appr…
Physics-informed machine learning for building performance simulation-A review of a nascent field
Zixin Jiang, Xuezheng Wang, Han Li +5
Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing performance of the built environment, optimizing energy efficiency, im…