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Optimal Parameterizing Manifolds for Anticipating Tipping Points and Higher-Order Critical Transitions
Transitions in Stochastic Non-Equilibrium Systems: Efficient Reduction and Analysis
Deep Reinforcement Learning for Adaptive Mesh Refinement
Bayesian Learning of Coupled Biogeochemical-Physical Models
Generalized Neural Closure Models with Interpretability
Confidently Comparing Estimates with the c-value
Generic Generation of Noise-Driven Chaos in Stochastic Time Delay Systems: Bridging the Gap with High-End Simulations
Stochastic Rectification of Fast Oscillations on Slow Manifold Closures
Neural Closure Models for Dynamical Systems
Reduced-order Models for Coupled Dynamical Systems: Data-driven Methods and the Koopman Operator
Conferences
Gaussian Processes at the Helm(holtz): A More Fluid Model for Ocean Currents
Evaluation of Deep Neural Operator Models toward Ocean Forecasting
The Exact Sample Complexity Gain from Invariances for Kernel Regression
On the Generalization of Learning Algorithms That Do Not Converge
Neural Closure Model for Dynamic Mode Decomposition Forecasts
Measuring the Robustness of Gaussian Processes to Kernel Choice
Measuring Generalization with Optimal Transport
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Generalization and Representational Limits of Graph Neural Networks
Sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems
On Leveraging Pretrained GANs for Generation with Limited Data
Pre-prints
The High-Frequency and Rare Events Barriers to Neural Closures of Atmospheric Dynamics
Turbulence Closure with Small, Local Neural Networks: Forced Two-dimensional and β-plane Flows
Deep Spectral Computations in Linear and Nonlinear Diffusion Problems
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