Search Results for author:
Found 15 papers, 4 papers with code
Dynamical-generative downscaling of climate model ensembles
Regional high-resolution climate projections are crucial for many applications, such as agriculture, hydrology, and natural hazard risk assessment.
Generative AI for fast and accurate Statistical Computation of Fluids
We present a generative AI algorithm for addressing the challenging task of fast, accurate and robust statistical computation of three-dimensional turbulent fluid flows.
Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme
Conventional WENO3 methods are known to be highly dissipative at lower resolutions, introducing significant errors in the pre-asymptotic regime.
Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models
We present Wideband back-projection diffusion, an end-to-end probabilistic framework for approximating the posterior distribution induced by the inverse scattering map from wideband scattering data.
A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data
In this work we present a general strategy for training neural network models to non-intrusively correct under-resolved long-time simulations of chaotic systems.
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems
Learning dynamics from dissipative chaotic systems is notoriously difficult due to their inherent instability, as formalized by their positive Lyapunov exponents, which exponentially amplify errors in the learned dynamics.
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems
In this work, we develop a probabilistic approximation scheme for the conditional score function which provably converges to the true distribution as the noise level decreases.
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
Moreover, our procedure correctly matches the statistics of physical quantities, even when the low-frequency content of the inputs and outputs do not match, a crucial but difficult-to-satisfy assumption needed by current state-of-the-art alternatives.
Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems
We present a data-driven, space-time continuous framework to learn surrogate models for complex physical systems described by advection-dominated partial differential equations.
Learning to correct spectral methods for simulating turbulent flows
We build upon Fourier-based spectral methods, which are known to be more efficient than other numerical schemes for simulating PDEs with smooth and periodic solutions.
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime
We propose an end-to-end deep learning framework that comprehensively solves the inverse wave scattering problem across all length scales.
Wide-band butterfly network: stable and efficient inversion via multi-frequency neural networks
We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data.
Efficient Long-Range Convolutions for Point Clouds
We showcase this framework by introducing a neural network architecture that combines LRC-layers with short-range convolutional layers to accurately learn the energy and force associated with a $N$-body potential.
Learning the mapping $\mathbf{x}\mapsto \sum_{i=1}^d x_i^2$: the cost of finding the needle in a haystack
When such structural information is not available, and we may only use a dense neural network, the optimization procedure to find the sparse network embedded in the dense network is similar to finding the needle in a haystack, using a given number of samples of the function.
Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks
By directly targeting at the self-consistent electron density, we demonstrate that the adapted network architecture, called the Deep Density, can effectively represent the electron density as the linear combination of contributions from many local clusters.
