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Found 6 papers, 3 papers with code
CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations
This work introduces reduced models based on Continuous Low Rank Adaptation (CoLoRA) that pre-train neural networks for a given partial differential equation and then continuously adapt low-rank weights in time to rapidly predict the evolution of solution fields at new physics parameters and new initial conditions.
Neuronal Temporal Filters as Normal Mode Extractors
Such variation in the temporal filter with input SNR resembles that observed experimentally in biological neurons.
Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes
This work focuses on the conservation of quantities such as Hamiltonians, mass, and momentum when solution fields of partial differential equations are approximated with nonlinear parametrizations such as deep networks.
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks
Training neural networks sequentially in time to approximate solution fields of time-dependent partial differential equations can be beneficial for preserving causality and other physics properties; however, the sequential-in-time training is numerically challenging because training errors quickly accumulate and amplify over time.
Representational dissimilarity metric spaces for stochastic neural networks
Quantifying similarity between neural representations -- e. g. hidden layer activation vectors -- is a perennial problem in deep learning and neuroscience research.
Bridging the Gap: Point Clouds for Merging Neurons in Connectomics
To address this problem, we propose a novel method based on point cloud representations of neurons.
