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08/09/2023
Finite Element Operator Network for Solving Parametric PDEs
Partial differential equations (PDEs) underlie our understanding and pre...
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12/02/2022
Sparse SPN: Depth Completion from Sparse Keypoints
Our long term goal is to use image-based depth completion to quickly cre...
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12/02/2022
QFF: Quantized Fourier Features for Neural Field Representations
Multilayer perceptrons (MLPs) learn high frequencies slowly. Recent appr...
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10/14/2022
Deep PatchMatch MVS with Learned Patch Coplanarity, Geometric Consistency and Adaptive Pixel Sampling
Recent work in multi-view stereo (MVS) combines learnable photometric sc...
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07/05/2022
opPINN: Physics-Informed Neural Network with operator learning to approximate solutions to the Fokker-Planck-Landau equation
We propose a hybrid framework opPINN: physics-informed neural network (P...
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01/28/2022
Pseudo-Differential Integral Operator for Learning Solution Operators of Partial Differential Equations
Learning mapping between two function spaces has attracted considerable ...
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11/19/2021
DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering
DIVeR builds on the key ideas of NeRF and its variants – density models ...
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11/09/2021
Solving PDE-constrained Control Problems using Operator Learning
The modeling and control of complex physical dynamics are essential in r...
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08/19/2021
PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility
Recent learning-based multi-view stereo (MVS) methods show excellent per...
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09/28/2020
The model reduction of the Vlasov-Poisson-Fokker-Planck system to the Poisson-Nernst-Planck system via the Deep Neural Network Approach
The model reduction of a mesoscopic kinetic dynamics to a macroscopic co...
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11/22/2019