19 citations · 32 across the 10 of their papers we have counts for
10 papers
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming
Yongquan Qu, Mohamed Aziz Bhouri, Pierre Gentine
Accurate representations of unknown and sub-grid physical processes through parameterizations (or closure) in numerical simulations with quantified uncertainty are critical for res…
Stress-testing the coupled behavior of hybrid physics-machine learning climate simulations on an unseen, warmer climate
Jerry Lin, Mohamed Aziz Bhouri, Tom Beucler +2
Accurate and computationally-viable representations of clouds and turbulence are a long-standing challenge for climate model development. Traditional parameterizations that crudely…
Multi-fidelity climate model parameterization for better generalization and extrapolation
Mohamed Aziz Bhouri, Liran Peng, Michael S. Pritchard +1
Machine-learning-based parameterizations (i.e. representation of sub-grid processes) of global climate models or turbulent simulations have recently been proposed as a powerful alt…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks
Mohamed Aziz Bhouri, Michael Joly, Robert Yu +2
Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable…
History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96
Mohamed Aziz Bhouri, Pierre Gentine
Physical parameterizations are used as representations of unresolved subgrid processes within weather and global climate models or coarse-scale turbulent models, whose resolutions…
Fast PDE-constrained optimization via self-supervised operator learning
Sifan Wang, Mohamed Aziz Bhouri, Paris Perdikaris
Design and optimal control problems are among the fundamental, ubiquitous tasks we face in science and engineering. In both cases, we aim to represent and optimize an unknown (blac…
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data
Mohamed Aziz Bhouri, Paris Perdikaris
This paper presents a machine learning framework (GP-NODE) for Bayesian systems identification from partial, noisy and irregular observations of nonlinear dynamical systems. The pr…
Model-Order-Reduction Approach for Structural Health Monitoring of Large Deployed Structures with Localized Operational Excitations
Mohamed Aziz Bhouri
We present a simulation-based classification approach for large deployed structures with localized operational excitations. The method extends the two-level Port-Reduced Reduced-Ba…
A Two-Level Parameterized Model-Order Reduction Approach for Time-Domain Elastodynamics
Mohamed Aziz Bhouri, Anthony T. Patera
We present a two-level parameterized Model Order Reduction (pMOR) technique for the linear hyperbolic Partial Differential Equation (PDE) of time-domain elastodynamics. In order to…