Search Results for author: Michael P. Brenner

Found 10 papers, 4 papers with code

Learning to correct spectral methods for simulating turbulent flows

2 code implementations1 Jul 2022 Gideon Dresdner, Dmitrii Kochkov, Peter Norgaard, Leonardo Zepeda-Núñez, Jamie A. Smith, Michael P. Brenner, Stephan Hoyer

Specifically, we develop ML-augmented spectral solvers for three model PDEs of fluid dynamics, which improve upon the accuracy of standard spectral solvers at the same resolution.

BIG-bench Machine Learning

Context-Aware Abbreviation Expansion Using Large Language Models

no code implementations NAACL 2022 Shanqing Cai, Subhashini Venugopalan, Katrin Tomanek, Ajit Narayanan, Meredith Ringel Morris, Michael P. Brenner

Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phrases are abbreviated aggressively as primarily word-initial letters.

Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal Images

no code implementations23 Jul 2021 Katy Blumer, Subhashini Venugopalan, Michael P. Brenner, Jon Kleinberg

We find that some target tasks are easily predicted irrespective of the source task, and that some other target tasks are more accurately predicted from correlated source tasks than from embeddings trained on the same task.

regression

Cascades and Reconnection in Interacting Vortex Filaments

no code implementations22 Feb 2021 Rodolfo Ostilla-Mónico, Ryan McKeown, Michael P. Brenner, Shmuel M. Rubinstein, Alain Pumir

We demonstrate that when the angle between the two tubes is close to $\pi/2$, the interaction between tubes leads to the formation of thin vortex sheets.

Fluid Dynamics

Variational Data Assimilation with a Learned Inverse Observation Operator

1 code implementation22 Feb 2021 Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, Stephan Hoyer

Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data.

Weather Forecasting

Machine learning accelerated computational fluid dynamics

no code implementations28 Jan 2021 Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva, Qing Wang, Michael P. Brenner, Stephan Hoyer

Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics.

BIG-bench Machine Learning

Learned discretizations for passive scalar advection in a 2-D turbulent flow

2 code implementations11 Apr 2020 Jiawei Zhuang, Dmitrii Kochkov, Yohai Bar-Sinai, Michael P. Brenner, Stephan Hoyer

The computational cost of fluid simulations increases rapidly with grid resolution.

Computational Physics Disordered Systems and Neural Networks Fluid Dynamics

Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry

no code implementations27 Nov 2018 Kevin McCloskey, Ankur Taly, Federico Monti, Michael P. Brenner, Lucy Colwell

The dataset bias makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding.

Data-driven discretization: a method for systematic coarse graining of partial differential equations

3 code implementations15 Aug 2018 Yohai Bar-Sinai, Stephan Hoyer, Jason Hickey, Michael P. Brenner

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

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