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Found 8 papers, 4 papers with code
PIC2O-Sim: A Physics-Inspired Causality-Aware Dynamic Convolutional Neural Operator for Ultra-Fast Photonic Device FDTD Simulation
However, FDTD is known for its prohibitive runtime cost, taking minutes to hours to simulate a single device.
Rare Event Probability Learning by Normalizing Flows
Next, it estimates the rare event probability by utilizing importance sampling in conjunction with the last proposal.
KirchhoffNet: A Scalable Ultra Fast Analog Neural Network
Essentially, KirchhoffNet is an analog circuit that can function as a neural network, utilizing its initial node voltages as the neural network input and the node voltages at a specific time point as the output.
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
In this work, for the first time, a physics-agnostic neural operator-based framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain Maxwell PDEs for ultra-fast parametric photonic device simulation.
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Data lies at the core of modern deep learning.
FreDo: Frequency Domain-based Long-Term Time Series Forecasting
Finally, we validate that the frequency domain is indeed better by comparing univariate models trained in the frequency v. s.
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
A common assumption in training neural networks via maximum likelihood estimation on time series is that the errors across time steps are uncorrelated.
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network
Unlike the classical variational inference, where a factorized distribution is used to approximate the posterior, we employ a feedforward neural network supplemented by an encoder recurrent neural network to develop a more flexible probabilistic model.
