Search Results for author:
Found 17 papers, 12 papers with code
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.
Neural General Circulation Models for Weather and Climate
Here we present the first GCM that combines a differentiable solver for atmospheric dynamics with ML components, and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best ML and physics-based methods.
WeatherBench 2: A benchmark for the next generation of data-driven global weather models
WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling.
GraphCast: Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
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.
Efficient and Modular Implicit Differentiation
In this paper, we propose automatic implicit differentiation, an efficient and modular approach for implicit differentiation of optimization problems.
Variational Data Assimilation with a Learned Inverse Observation Operator
Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data.
Machine learning accelerated computational fluid dynamics
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics.
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectures.
Learned discretizations for passive scalar advection in a 2-D turbulent flow
The computational cost of fluid simulations increases rapidly with grid resolution.
Computational Physics Disordered Systems and Neural Networks Fluid Dynamics
Lagrangian Neural Networks
Accurate models of the world are built upon notions of its underlying symmetries.
Inundation Modeling in Data Scarce Regions
Flood forecasts are crucial for effective individual and governmental protective action.
Neural reparameterization improves structural optimization
Structural optimization is a popular method for designing objects such as bridge trusses, airplane wings, and optical devices.
Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks
A key challenge in metasurface design is the development of algorithms that can effectively and efficiently produce high performance devices.
Data-driven discretization: a method for systematic coarse graining of partial differential equations
Many problems in theoretical physics are centered on representing the behavior of a physical theory at long wave lengths and slow frequencies by integrating out degrees of freedom which change rapidly in time and space.
Disordered Systems and Neural Networks Computational Physics
Correcting Nuisance Variation using Wasserstein Distance
One motivating application is drug development: morphological cell features can be captured from images, from which similarities between different drug compounds applied at different doses can be quantified.
The Cramer Distance as a Solution to Biased Wasserstein Gradients
We show that the Cram\'er distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences.
