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08/06/2023
Control-aware echo state networks (Ca-ESN) for the suppression of extreme events
Extreme event are sudden large-amplitude changes in the state or observa...
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06/19/2023
Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach
We propose the physics-constrained convolutional neural network (PC-CNN)...
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06/07/2023
Uncovering solutions from data corrupted by systematic errors: A physics-constrained convolutional neural network approach
Information on natural phenomena and engineering systems is typically co...
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06/07/2023
Inferring unknown unknowns: Regularized bias-aware ensemble Kalman filter
Because of physical assumptions and numerical approximations, reduced-or...
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05/24/2023
Short and Straight: Geodesics on Differentiable Manifolds
Manifolds discovered by machine learning models provide a compact repres...
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05/24/2023
Reconstruction, forecasting, and stability of chaotic dynamics from partial data
The forecasting and computation of the stability of chaotic systems from...
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03/27/2023
Quantum Multi-Model Fitting
Geometric model fitting is a challenging but fundamental computer vision...
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01/31/2023
Convolutional autoencoder for the spatiotemporal latent representation of turbulence
Turbulence is characterised by chaotic dynamics and a high-dimensional s...
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11/21/2022
Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network
The spatiotemporal dynamics of turbulent flows is chaotic and difficult ...
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11/15/2022
On interpretability and proper latent decomposition of autoencoders
The dynamics of a turbulent flow tend to occupy only a portion of the ph...
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10/31/2022
Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems
In the absence of high-resolution samples, super-resolution of sparse ob...
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10/28/2022
Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems
Measurements on dynamical systems, experimental or otherwise, are often ...
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08/19/2022
A Physics-based Domain Adaptation framework for modelling and forecasting building energy systems
State-of-the-art machine-learning based models are a popular choice for ...
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04/25/2022
Data-driven prediction and control of extreme events in a chaotic flow
An extreme event is a sudden and violent change in the state of a nonlin...
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01/20/2022
Statistical prediction of extreme events from small datasets
We propose Echo State Networks (ESNs) to predict the statistics of extre...
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06/17/2021
Gradient-free optimization of chaotic acoustics with reservoir computing
We develop a versatile optimization method, which finds the design param...
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06/11/2021
Real-time thermoacoustic data assimilation
Low-order thermoacoustic models are qualitatively correct, but they are ...
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02/15/2021
Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach
We propose a physics-constrained machine learning method-based on reserv...
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12/20/2020
Auto-Encoded Reservoir Computing for Turbulence Learning
We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn...
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01/09/2020
Learning ergodic averages in chaotic systems
We propose a physics-informed machine learning method to predict the tim...
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01/06/2020
Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach
We extend the Physics-Informed Echo State Network (PI-ESN) framework to ...
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12/23/2019
A physics-aware machine to predict extreme events in turbulence
We propose a physics-aware machine learning method to time-accurately pr...
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04/09/2019
Data assimilation in a nonlinear time-delayed dynamical system
When the heat released by a flame is sufficiently in phase with the acou...
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04/09/2019
