Search Results for author:
Found 7 papers, 2 papers with code
Evaluation of Deep Neural Operator Models toward Ocean Forecasting
The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics.
Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers
We demonstrate the safety of our approach in such realistic situations empirically with large-scale simulations of a vessel navigating in high-risk regions in the Northeast Pacific.
Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underactuated Systems Navigating on Uncertain Ocean Currents
We propose a dynamic programming-based method to efficiently solve for the optimal growth value function when true currents are known.
Generalized Neural Closure Models with Interpretability
Improving the predictive capability and computational cost of dynamical models is often at the heart of augmenting computational physics with machine learning (ML).
Bayesian Learning of Coupled Biogeochemical-Physical Models
We develop a Bayesian model learning methodology that allows interpolation in the space of candidate models and discovery of new models from noisy, sparse, and indirect observations, all while estimating state fields and parameter values, as well as the joint PDFs of all learned quantities.
Deep Reinforcement Learning for Adaptive Mesh Refinement
Finite element discretizations of problems in computational physics often rely on adaptive mesh refinement (AMR) to preferentially resolve regions containing important features during simulation.
Neural Closure Models for Dynamical Systems
The new "neural closure models" augment low-fidelity models with neural delay differential equations (nDDEs), motivated by the Mori-Zwanzig formulation and the inherent delays in complex dynamical systems.
