30 papers
Confidence intervals for functionals in constrained inverse problems via data-adaptive sampling-based calibration
Michael Stanley, Pau Batlle, Pratik Patil +2
We address functional uncertainty quantification for ill-posed inverse problems where it is possible to evaluate a possibly rank-deficient forward model, the observation noise dist…
Graphical conditional generative modeling for digital twin modeling
Zongren Zou, Théo Bourdais, Ricardo Baptista +1
Digital twin modeling, including control and data assimilation under model uncertainty, often faces an open-ended fidelity problem: adding variables, data streams, and time scales…
Aircast-Mars: A Mars Foundation Model for Global Weather Forecasting with HEALPix-Aware Convolutions
Manmeet Singh, Saptarishi Dhanuka, Naveen Sudharsan +3
Foundation models for planetary atmospheres promise fast, lightweight surrogates of expensive general circulation models (GCMs) for mission planning and scientific inquiry. Here we…
Structure-Preserving Neural Surrogates with Tractable Uncertainty Quantification
Handi Zhang, Adrienne M. Propp, Brooks Kinch +2
Recent advances in scientific machine learning provide a means of near-real-time solution to partial differential equations (PDEs), but lack the theoretical underpinnings of conven…
Pruning Deep Neural Networks via the Marchenko--Pastur Distribution
Leonid Berlyand, Theo Bourdais, Houman Owhadi +1
We study a Marchenko--Pastur (MP) random-matrix approach to pruning deep neural networks with very small post-pruning fine-tuning budgets. The main practical contribution is accura…
ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks
Zhizhen Zhang, Hyemin Gu, Benjamin J. Zhang +6
Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved. We introduce ISOMORPH, the first public di…