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Deep reinforcement learning (DRL) has recently emerged as a promising tool for Dynamic Algorithm Configuration (DAC), enabling evolutionary algorithms to adapt their parameters online rather than relying on static tuned configurations.…

Optimization and Control · Mathematics 2026-04-03 Andrea Mencaroni , Robbert Reijnen , Yingqian Zhang , Dieter Claeys

We present that, instead of establishing the equations of motion, one can model-freely reveal the dynamical properties of a black-box system using a learning machine. Trained only by a segment of time series of a state variable recorded at…

Machine Learning · Computer Science 2022-04-15 Hong Zhao

There has been a growing interest in developing data-driven, and in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these…

Signal Processing · Electrical Eng. & Systems 2022-05-11 Haoran Sun , Wenqiang Pu , Xiao Fu , Tsung-Hui Chang , Mingyi Hong

We propose a reinforcement learning strategy to control wind turbine energy generation by actively changing the rotor speed, the rotor yaw angle and the blade pitch angle. A double deep Q-learning with a prioritized experience replay agent…

Machine Learning · Computer Science 2024-02-20 Daniel Soler , Oscar Mariño , David Huergo , Martín de Frutos , Esteban Ferrer

Friction drag from a turbulent fluid moving past or inside an object plays a crucial role in domains as diverse as transportation, public utility infrastructure, energy technology, and human health. As a direct measure of the shear-induced…

Fluid Dynamics · Physics 2023-10-19 Esther Lagemann , Steven L. Brunton , Christian Lagemann

For robots operating in the real world, it is desirable to learn reusable behaviours that can effectively be transferred and adapted to numerous tasks and scenarios. We propose an approach to learn abstract motor skills from data using a…

Hybrid aerial--ground robots offer both traversability and endurance, but stair-like discontinuities create a trade-off: wheels alone often stall at edges, while flight is energy-hungry for small height gains. We propose an energy-aware…

Robotics · Computer Science 2026-03-31 Jiaxing Li , Wen Tian , Xinhang Xu , Junbin Yuan , Sebastian Scherer , Muqing Cao

Scaling laws in deep learning -- empirical power-law relationships linking model performance to resource growth -- have emerged as simple yet striking regularities across architectures, datasets, and tasks. These laws are particularly…

Machine Learning · Computer Science 2026-05-01 Francesco D'Amico , Dario Bocchi , Matteo Negri

The autonomous operation of flexible-wing aircraft is technically challenging and has never been presented within literature. The lack of an exact modeling framework is due to the complex nonlinear aerodynamic relationships governed by the…

Systems and Control · Electrical Eng. & Systems 2021-03-31 Mohammed Abouheaf , Nathaniel Mailhot , Wail Gueaieb , Davide Spinello

Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning. The most common way to estimate dynamics is by fitting a one-step ahead prediction model and using it to recursively…

Machine Learning · Computer Science 2021-09-02 Nathan O. Lambert , Albert Wilcox , Howard Zhang , Kristofer S. J. Pister , Roberto Calandra

We present a framework for model-free learning of event-triggered control strategies. Event-triggered methods aim to achieve high control performance while only closing the feedback loop when needed. This enables resource savings, e.g.,…

Systems and Control · Electrical Eng. & Systems 2021-04-26 Niklas Funk , Dominik Baumann , Vincent Berenz , Sebastian Trimpe

This paper introduces a novel data-driven hierarchical control scheme for managing a fleet of nonlinear, capacity-constrained autonomous agents in an iterative environment. We propose a control framework consisting of a high-level dynamic…

Robotics · Computer Science 2024-04-12 Charlott Vallon , Alessandro Pinto , Bartolomeo Stellato , Francesco Borrelli

We propose a joint simulation and real-world learning framework for mapping navigation instructions and raw first-person observations to continuous control. Our model estimates the need for environment exploration, predicts the likelihood…

Robotics · Computer Science 2019-10-23 Valts Blukis , Yannick Terme , Eyvind Niklasson , Ross A. Knepper , Yoav Artzi

This paper considers the problem of designing a continuous-time dynamical system that solves a constrained nonlinear optimization problem and makes the feasible set forward invariant and asymptotically stable. The invariance of the feasible…

Optimization and Control · Mathematics 2024-08-27 Ahmed Allibhoy , Jorge Cortés

Scaling laws describe how learning performance improves with data, compute, or training time, and have become a central theme in modern deep learning. We study this phenomenon in a canonical nonlinear model: phase retrieval with anisotropic…

Machine Learning · Statistics 2025-11-25 Guillaume Braun , Bruno Loureiro , Ha Quang Minh , Masaaki Imaizumi

The three-dimensional dynamics of a single non-Brownian flexible fiber in shear flow is evaluated numerically, in the absence of inertia. A wide range of ratios A of bending to hydrodynamic forces and hundreds of initial configurations are…

Soft Condensed Matter · Physics 2020-02-12 A. M. Slowicka , H. A. Stone , M. L. Ekiel-Jezewska

An approach to control tethered wings for airborne wind energy is proposed. A fixed length of the lines is considered, and the aim of the control system is to obtain figure-eight crosswind trajectories. The proposed technique is based on…

Systems and Control · Computer Science 2013-08-27 Lorenzo Fagiano , Aldo U. Zgraggen , Manfred Morari , Mustafa Khammash

Inspired by the wake-surfing nature of animals, this study aims to understand the aerodynamic force variation on a wing surfing in an unsteady 2-D wake. Wind tunnel experiments were conducted using Particle Image Velocimetry (PIV) and force…

Fluid Dynamics · Physics 2025-12-11 Siyang Hao , Kenneth Breuer

We study the fully nonlinear, nonlocal dynamics of two-dimensional multicomponent vesicles in a shear flow with matched viscosity of the inner and outer fluids. Using a nonstiff, pseudo-spectral boundary integral method, we investigate…

Soft Condensed Matter · Physics 2016-11-01 Kai Liu , Gary R. Marple , Shuwang Li , Shravan Veerapaneni , John Lowengrub

Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors and solve diverse…

Machine Learning · Computer Science 2020-02-18 Archit Sharma , Shixiang Gu , Sergey Levine , Vikash Kumar , Karol Hausman