Publications by Roland Schwan | EPFL Graph Search

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Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

Recent advances in Model Predictive Control (MPC) algorithms and methodologies, combined with the surge of computational power of available embedded platforms, allows the use of real-time optimization-based control of fast mechatronic systems. This paper p ...

We propose a framework for the stability verification of Mixed-Integer Linear Programming (MILP) representable control policies. This framework compares a fixed candidate policy, which admits an efficient parameterization and can be evaluated at a low comp ...

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 Method of Multipliers (PMM), the algorithm can handle ill-conditioned convex QP p ...

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 - possibly different - convex Lyapunov functions. We show that this implies the ...

We present the identification of the non-linear dynamics of a novel hovercraft design, employing end-to-end deep learning techniques. Our experimental setup consists of a hovercraft propelled by racing drone propellers mounted on a lightweight foam base, a ...

Roland Schwan

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Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Optimal Thrust Vector Control of an Electric Small-Scale Rocket Prototype

Stability Verification of Neural Network Controllers using Mixed-Integer Programming

PIQP: A Proximal Interior-Point Quadratic Programming Solver

Optimal Thrust Vector Control of an Electric Small-Scale Rocket Prototype

On Continuation and Convex Lyapunov Functions

On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach