Search Results for author: Guido Novati

Found 8 papers, 4 papers with code

Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

1 code implementation7 Sep 2022 Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Mark S. Graham, Pedro Borges, Virginia Fernandez, Dai Yang, Jeremy Appleyard, Guido Novati, Disha Mehra, Mike Vella, Parashkev Nachev, Sebastien Ourselin, Jorge Cardoso

Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations.

Anatomy Anomaly Detection

Learning swimming escape patterns for larval fish under energy constraints

no code implementations3 May 2021 Ioannis Mandralis, Pascal Weber, Guido Novati, Petros Koumoutsakos

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.

reinforcement-learning Reinforcement Learning (RL) +1

Learning Efficient Navigation in Vortical Flow Fields

no code implementations21 Feb 2021 Peter Gunnarson, Ioannis Mandralis, Guido Novati, Petros Koumoutsakos, John O. Dabiri

Efficient point-to-point navigation in the presence of a background flow field is important for robotic applications such as ocean surveying.

reinforcement-learning Reinforcement Learning (RL)

Improved Memories Learning

no code implementations24 Aug 2020 Francesco Varoli, Guido Novati, Pantelis R. Vlachas, Petros Koumoutsakos

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.

Reinforcement Learning (RL)

Automating Turbulence Modeling by Multi-Agent Reinforcement Learning

1 code implementation18 May 2020 Guido Novati, Hugues Lascombes de Laroussilhe, Petros Koumoutsakos

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

Remember and Forget for Experience Replay

2 code implementations ICLR 2019 Guido Novati, Petros Koumoutsakos

ER recalls experiences from past iterations to compute gradient estimates for the current policy, increasing data-efficiency.

Policy Gradient Methods Q-Learning +1

Deep-Reinforcement-Learning for Gliding and Perching Bodies

1 code implementation7 Jul 2018 Guido Novati, Lakshminarayanan Mahadevan, Petros Koumoutsakos

Controlled gliding is one of the most energetically efficient modes of transportation for natural and human powered fliers.

Robotics

Efficient collective swimming by harnessing vortices through deep reinforcement learning

no code implementations7 Feb 2018 Siddhartha Verma, Guido Novati, Petros Koumoutsakos

Fish in schooling formations navigate complex flow-fields replete with mechanical energy in the vortex wakes of their companions.

Navigate reinforcement-learning +1

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