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Found 30 papers, 15 papers with code
Inclusion in CSR Reports: The Lens from a Data-Driven Machine Learning Model
Inclusion, as one of the foundations in the diversity, equity, and inclusion initiative, concerns the degree of being treated as an ingroup member in a workplace.
Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration
Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media.
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks.
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling.
Predicting parametric spatiotemporal dynamics by multi-resolution PDE structure-preserved deep learning
A more promising way is to leverage our prior physics knowledge in scientific deep learning models, known as physics-informed deep learning (PiDL).
Importance is in your attention: agent importance prediction for autonomous driving
Trajectory prediction is an important task in autonomous driving.
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
Deep neural operators can learn operators mapping between infinite-dimensional function spaces via deep neural networks and have become an emerging paradigm of scientific machine learning.
Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate
We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments.
MIONet: Learning multiple-input operators via tensor product
Based on our theory and a low-rank approximation, we propose a novel neural operator, MIONet, to learn multiple-input operators.
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks
The dynamics of systems biological processes are usually modeled by a system of ordinary differential equations (ODEs) with many unknown parameters that need to be inferred from noisy and sparse measurements.
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
We tested gPINNs extensively and demonstrated the effectiveness of gPINNs in both forward and inverse PDE problems.
Active noise control techniques for nonlinear systems
Most of the literature focuses on the development of the linear active noise control (ANC) techniques.
A survey on active noise control techniques -- Part I: Linear systems
Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems.
Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation
In addition, an adaptive approach for the choice of the thresholding parameter in the proximal step is also proposed based on the minimization of the mean square deviation.
One-shot learning for solution operators of partial differential equations
Discovering governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in a variety of areas of science and engineering.
Physics-informed neural networks with hard constraints for inverse design
We achieve the same objective as conventional PDE-constrained optimization methods based on adjoint methods and numerical PDE solvers, but find that the design obtained from hPINN is often simpler and smoother for problems whose solution is not unique.
Mixed graphs with smallest eigenvalue greater than $-\frac{\sqrt{5}+1}{2}$
The classical problem of characterizing the graphs with bounded eigenvalues may date back to the work of Smith in 1970.
Combinatorics 05C50
Operator learning for predicting multiscale bubble growth dynamics
Simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs).
Computational Physics
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition
In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition.
Physics-informed neural networks for inverse problems in nano-optics and metamaterials
In this paper we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies.
Computational Physics Optics
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
This universal approximation theorem is suggestive of the potential application of neural networks in learning nonlinear operators from data.
DeepXDE: A deep learning library for solving differential equations
We also present a Python library for PINNs, DeepXDE, which is designed to serve both as an education tool to be used in the classroom as well as a research tool for solving problems in computational science and engineering.
Gated Multiple Feedback Network for Image Super-Resolution
The rapid development of deep learning (DL) has driven single image super-resolution (SR) into a new era.
Ranked #14 on
Image Super-Resolution
on Manga109 - 4x upscaling
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness
To derive a meaningful bound, we study the generalization error of neural networks for classification problems in terms of data distribution and neural network smoothness.
Dying ReLU and Initialization: Theory and Numerical Examples
Numerical examples are provided to demonstrate the effectiveness of the new initialization procedure.
How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition
It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved.
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Here, we propose a new method with the objective of endowing the DNN with uncertainty quantification for both sources of uncertainty, i. e., the parametric uncertainty and the approximation uncertainty.
Collapse of Deep and Narrow Neural Nets
However, here we show that even for such activation, deep and narrow neural networks (NNs) will converge to erroneous mean or median states of the target function depending on the loss with high probability.
