Search Results for author:
Found 26 papers, 8 papers with code
Path planning of magnetic microswimmers in high-fidelity simulations of capillaries with deep reinforcement learning
Biomedical applications such as targeted drug delivery, microsurgery or sensing rely on reaching precise areas within the body in a minimally invasive way.
Generative Learning for Forecasting the Dynamics of Complex Systems
We introduce generative models for accelerating simulations of complex systems through learning and evolving their effective dynamics.
Closure Discovery for Coarse-Grained Partial Differential Equations using Multi-Agent Reinforcement Learning
However, simulations that capture the full range of spatio-temporal scales in such PDEs are often prohibitively expensive.
Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence
Reinforcement learning is emerging as a potent alternative for developing such closures as it requires only low-order statistics and leads to stable closures.
Interpretable learning of effective dynamics for multiscale systems
In recent years, techniques based on deep recurrent neural networks have produced promising results for the modeling and simulation of complex spatiotemporal systems and offer large flexibility in model development as they can incorporate experimental and computational data.
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning
In this study, we tackle this challenge by introducing an off-policy inverse multi-agent reinforcement learning algorithm (IMARL).
Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems
To the best of our knowledge, AdaLED is the first framework that couples a surrogate model with a computational solver to achieve online adaptive learning of effective dynamics.
Interpretable reduced-order modeling with time-scale separation
To this end, we combine a non-linear autoencoder architecture with a time-continuous model for the latent dynamics in the complex space.
Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts
Recurrent Neural Networks (RNNs) have become an integral part of modeling and forecasting frameworks in areas like natural language processing and high-dimensional dynamical systems such as turbulent fluid flows.
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning
We present the extension of the Remember and Forget for Experience Replay (ReF-ER) algorithm to Multi-Agent Reinforcement Learning (MARL).
Scientific multi-agent reinforcement learning for wall-models of turbulent flows
We believe that SciMARL creates unprecedented capabilities for the simulation of turbulent flows.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning swimming escape patterns for larval fish under energy constraints
The present, data efficient, reinforcement learning algorithm results in an array of patterns that reveal practical flow optimization principles for efficient swimming and the methodology can be transferred to the control of aquatic robotic devices operating under energy constraints.
Learning Efficient Navigation in Vortical Flow Fields
Efficient point-to-point navigation in the presence of a background flow field is important for robotic applications such as ocean surveying.
Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics
We believe that the proposed framework provides a dramatic increase to simulation capabilities and opens new horizons for the effective modeling of complex molecular systems.
Improved Memories Learning
We propose Improved Memories Learning (IMeL), a novel algorithm that turns reinforcement learning (RL) into a supervised learning (SL) problem and delimits the role of neural networks (NN) to interpolation.
Multiscale Simulations of Complex Systems by Learning their Effective Dynamics
Here we present a novel systematic framework that bridges large scale simulations and reduced order models to Learn the Effective Dynamics (LED) of diverse complex systems.
Automating Turbulence Modeling by Multi-Agent Reinforcement Learning
The modeling of turbulent flows is critical to scientific and engineering problems ranging from aircraft design to weather forecasting and climate prediction.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics
We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (RC) and Backpropagation through time (BPTT) for gated network architectures.
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales.
Remember and Forget for Experience Replay
ER recalls experiences from past iterations to compute gradient estimates for the current policy, increasing data-efficiency.
Deep-Reinforcement-Learning for Gliding and Perching Bodies
Controlled gliding is one of the most energetically efficient modes of transportation for natural and human powered fliers.
Robotics
Personalized Radiotherapy Design for Glioblastoma Using Mathematical Models, Multimodal Scans and Bayesian Inference
Here we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans.
Computational Engineering, Finance, and Science
Data-assisted reduced-order modeling of extreme events in complex dynamical systems
In this way, the data-driven model improves the imperfect model in regions where data is available, while for locations where data is sparse the imperfect model still provides a baseline for the prediction of the system dynamics.
Chaotic Dynamics Computational Physics
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks.
Efficient collective swimming by harnessing vortices through deep reinforcement learning
Fish in schooling formations navigate complex flow-fields replete with mechanical energy in the vortex wakes of their companions.
ContextVP: Fully Context-Aware Video Prediction
Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions.
