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Found 22 papers, 5 papers with code
PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images
Extensively evaluating methods with seven image recognition benchmarks, we show that the proposed PAC-FNO improves the performance of existing baseline models on images with various resolutions by up to 77. 1% and various types of natural variations in the images at inference.
Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer
These methods are typically designed to i) reduce the computational cost in solving physical dynamics and/or ii) propose techniques to enhance the solution accuracy in fluid and rigid body dynamics.
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs), one of the most influential works of the differential equation-based deep learning, are to continuously generalize residual networks and opened a new field.
Graph Convolutions Enrich the Self-Attention in Transformers!
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc.
Reversible and irreversible bracket-based dynamics for deep graph neural networks
Recent works have shown that physics-inspired architectures allow the training of deep graph neural networks (GNNs) without oversmoothing.
Time Series Forecasting with Hypernetworks Generating Parameters in Advance
Forecasting future outcomes from recent time series data is not easy, especially when the future data are different from the past (i. e. time series are under temporal drifts).
Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting
However, parameterization of dynamics using a neural network makes it difficult for humans to identify causal structures in the data.
Parameter-varying neural ordinary differential equations with partition-of-unity networks
In this study, we propose parameter-varying neural ordinary differential equations (NODEs) where the evolution of model parameters is represented by partition-of-unity networks (POUNets), a mixture of experts architecture.
AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation
In this work, we propose adaptive momentum estimation neural ODEs (AdamNODEs) that adaptively control the acceleration of the classical momentum-based approach.
Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery
We introduce physics-informed multimodal autoencoders (PIMA) - a variational inference framework for discovering shared information in multimodal scientific datasets representative of high-throughput testing.
Climate Modeling with Neural Diffusion Equations
On the other hand, neural ordinary differential equations (NODEs) are to learn a latent governing equation of ODE from data.
Regularizing Image Classification Neural Networks with Partial Differential Equations
In other words, the knowledge contained by the learned governing equation can be injected into the neural network which approximates the PDE solution function.
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling
Discovery of dynamical systems from data forms the foundation for data-driven modeling and recently, structure-preserving geometric perspectives have been shown to provide improved forecasting, stability, and physical realizability guarantees.
Probabilistic partition of unity networks: clustering based deep approximation
We enrich POU-Nets with a Gaussian noise model to obtain a probabilistic generalization amenable to gradient-based minimization of a maximum likelihood loss.
Machine learning structure preserving brackets for forecasting irreversible processes
Forecasting of time-series data requires imposition of inductive biases to obtain predictive extrapolation, and recent works have imposed Hamiltonian/Lagrangian form to preserve structure for systems with reversible dynamics.
Partition of unity networks: deep hp-approximation
Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity and monomials.
Neural Partial Differential Equations
Neural ordinary differential equations (neural ODEs) introduced an approach to approximate a neural network as a system of ODEs after considering its layer as a continuous variable and discretizing its hidden dimension.
DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation
We present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs).
Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems
This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs.
Deep Conservation: A latent dynamics model for exact satisfaction of physical conservation laws
In contrast to existing methods for latent dynamics learning, this is the only method that both employs a nonlinear embedding and computes dynamics for the latent state that guarantee the satisfaction of prescribed physical properties.
Computational Physics
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling
0-1 knapsack is of fundamental importance in computer science, business, operations research, etc.
MMGAN: Manifold Matching Generative Adversarial Network
MMGAN finds two manifolds representing the vector representations of real and fake images.
