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Found 10 papers, 4 papers with code
Learning to correct spectral methods for simulating turbulent flows
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.
Context-Aware Abbreviation Expansion Using Large Language Models
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
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.
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment.
Cascades and Reconnection in Interacting Vortex Filaments
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
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.
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
Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry
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
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
