4 citations · 11 across the 9 of their papers we have counts for
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eess.SY2023★ 2 cited
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
math.OC2023★ 2 cited
PIQP: A Proximal Interior-Point Quadratic Programming Solver
Roland Schwan, Yuning Jiang, Daniel Kuhn +1
This paper presents PIQP, a high-performance toolkit for solving generic sparse quadratic programs (QP). Combining an infeasible Interior Point Method (IPM) with the Proximal Metho…
math.OC2023★ 4 cited
On continuation and convex Lyapunov functions
Wouter Jongeneel, Roland Schwan
Suppose that the origin is globally asymptotically stable under a set of continuous vector fields on Euclidean space and suppose that all those vector fields come equipped with --…