Search Results for author:
Found 13 papers, 5 papers with code
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems
Machine learning methods for the construction of data-driven reduced order model models are used in an increasing variety of engineering domains, especially as a supplement to expensive computational fluid dynamics for design problems.
Active Learning for Optimal Intervention Design in Causal Models
Here, we develop a causal active learning strategy to identify interventions that are optimal, as measured by the discrepancy between the post-interventional mean of the distribution and a desired target mean.
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models
This ensures that key information is never wasted in testing or validation.
Discovering and forecasting extreme events via active learning in neural operators
This model-agnostic framework pairs a BED scheme that actively selects data for quantifying extreme events with an ensemble of DNOs that approximate infinite-dimensional nonlinear operators.
Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression
We propose two bounded comparison metrics that may be implemented to arbitrary dimensions in regression tasks.
Machine-learning energy-preserving nonlocal closures for turbulent fluid flows and inertial tracers
Overall the adoption of the constraint results in an average improvement of 26% for one-dimensional closures and 29% for two-dimensional closures, being notably larger for flows that were not used for training.
Fluid Dynamics
Sparse Methods for Automatic Relevance Determination
This work considers methods for imposing sparsity in Bayesian regression with applications in nonlinear system identification.
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.
Learning the Tangent Space of Dynamical Instabilities from Data
For a large class of dynamical systems, the optimally time-dependent (OTD) modes, a set of deformable orthonormal tangent vectors that track directions of instabilities along any trajectory, are known to depend "pointwise" on the state of the system on the attractor, and not on the history of the trajectory.
Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples
For many important problems the quantity of interest is an unknown function of the parameters, which is a random vector with known statistics.
A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems
The 'next-best' design point is sequentially determined through an optimization procedure that selects the point in parameter space that maximally reduces uncertainty between the estimated bounds of the pdf prediction.
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.
