Saved in:
| Main Authors: | Du, Weitao, Liu, Shengchao, Zhang, Xuecang |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.01801 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
by: Zhang, Tianyu, et al.
Published: (2021)
by: Zhang, Tianyu, et al.
Published: (2021)
InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames
by: Li, Haorui, et al.
Published: (2025)
by: Li, Haorui, et al.
Published: (2025)
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
by: Liu, Shengchao, et al.
Published: (2023)
by: Liu, Shengchao, et al.
Published: (2023)
Flow Along the K-Amplitude for Generative Modeling
by: Du, Weitao, et al.
Published: (2025)
by: Du, Weitao, et al.
Published: (2025)
Gate-level boolean evolutionary geometric attention neural networks
by: Shi, Xianshuai, et al.
Published: (2025)
by: Shi, Xianshuai, et al.
Published: (2025)
SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning
by: Wu, Hongfei, et al.
Published: (2024)
by: Wu, Hongfei, et al.
Published: (2024)
Quantum-inspired tensor networks in machine learning models
by: Valverde, Guillermo, et al.
Published: (2026)
by: Valverde, Guillermo, et al.
Published: (2026)
Self-adaptive weighting and sampling for physics-informed neural networks
by: Chen, Wenqian, et al.
Published: (2025)
by: Chen, Wenqian, et al.
Published: (2025)
Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis
by: Li, Dong, et al.
Published: (2026)
by: Li, Dong, et al.
Published: (2026)
Graph neural network for colliding particles with an application to sea ice floe modeling
by: Zhu, Ruibiao
Published: (2026)
by: Zhu, Ruibiao
Published: (2026)
Frequency-Forcing: From Scaling-as-Time to Soft Frequency Guidance
by: Du, Weitao
Published: (2026)
by: Du, Weitao
Published: (2026)
Learning Fourier shapes to probe the geometric world of deep neural networks
by: Wang, Jian, et al.
Published: (2025)
by: Wang, Jian, et al.
Published: (2025)
Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
by: Straub, Christopher, et al.
Published: (2025)
by: Straub, Christopher, et al.
Published: (2025)
PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks
by: Almanstötter, Marius, et al.
Published: (2025)
by: Almanstötter, Marius, et al.
Published: (2025)
Astral: training physics-informed neural networks with error majorants
by: Fanaskov, Vladimir, et al.
Published: (2024)
by: Fanaskov, Vladimir, et al.
Published: (2024)
Permutation-equivariant quantum convolutional neural networks
by: Das, Sreetama, et al.
Published: (2024)
by: Das, Sreetama, et al.
Published: (2024)
Physics-informed neural networks need a physicist to be accurate: the case of mass and heat transport in Fischer-Tropsch catalyst particles
by: Nikolaienko, Tymofii, et al.
Published: (2024)
by: Nikolaienko, Tymofii, et al.
Published: (2024)
Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
by: Es'kin, Vasiliy A., et al.
Published: (2025)
by: Es'kin, Vasiliy A., et al.
Published: (2025)
A reduced-order derivative-informed neural operator for subsurface fluid-flow
by: Park, Jeongjin, et al.
Published: (2025)
by: Park, Jeongjin, et al.
Published: (2025)
Rhythmic sharing: A bio-inspired paradigm for zero-shot adaptive learning in neural networks
by: Kang, Hoony, et al.
Published: (2025)
by: Kang, Hoony, et al.
Published: (2025)
Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
by: Liu, Shengchao, et al.
Published: (2024)
by: Liu, Shengchao, et al.
Published: (2024)
Image space formalism of convolutional neural networks for k-space interpolation
by: Dawood, Peter, et al.
Published: (2024)
by: Dawood, Peter, et al.
Published: (2024)
On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
by: Li, Yuhao, et al.
Published: (2026)
by: Li, Yuhao, et al.
Published: (2026)
Breakeven complexity: A new perspective on neural partial differential equation solvers
by: Zhang, Yijing, et al.
Published: (2026)
by: Zhang, Yijing, et al.
Published: (2026)
Variational autoencoder-based neural network model compression
by: Cheng, Liang, et al.
Published: (2024)
by: Cheng, Liang, et al.
Published: (2024)
Restricting to the chip architecture maintains the quantum neural network accuracy
by: Friedrich, Lucas, et al.
Published: (2022)
by: Friedrich, Lucas, et al.
Published: (2022)
Task complexity shapes internal representations and robustness in neural networks
by: Jankowski, Robert, et al.
Published: (2025)
by: Jankowski, Robert, et al.
Published: (2025)
Spatiotemporal Field Generation Based on Hybrid Mamba-Transformer with Physics-informed Fine-tuning
by: Du, Peimian, et al.
Published: (2025)
by: Du, Peimian, et al.
Published: (2025)
Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks
by: Hamreras, Safa, et al.
Published: (2025)
by: Hamreras, Safa, et al.
Published: (2025)
Improving ideal MHD equilibrium accuracy with physics-informed neural networks
by: Thun, Timo, et al.
Published: (2025)
by: Thun, Timo, et al.
Published: (2025)
Bayesian neural networks via MCMC: a Python-based tutorial
by: Chandra, Rohitash, et al.
Published: (2023)
by: Chandra, Rohitash, et al.
Published: (2023)
Interpolating neural network: A novel unification of machine learning and interpolation theory
by: Park, Chanwook, et al.
Published: (2024)
by: Park, Chanwook, et al.
Published: (2024)
Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization
by: Zhang, Kaiwei, et al.
Published: (2024)
by: Zhang, Kaiwei, et al.
Published: (2024)
Updating the standard neuron model in artificial neural networks
by: Mohedano, Raul, et al.
Published: (2026)
by: Mohedano, Raul, et al.
Published: (2026)
Quality-factor inspired deep neural network solver for solving inverse scattering problems
by: Du, Yutong, et al.
Published: (2025)
by: Du, Yutong, et al.
Published: (2025)
A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
by: Goldshlager, Gil, et al.
Published: (2024)
by: Goldshlager, Gil, et al.
Published: (2024)
Kinematic analysis of structural mechanics based on convolutional neural network
by: Zhang, Leye, et al.
Published: (2024)
by: Zhang, Leye, et al.
Published: (2024)
BYE: Build Your Encoder with One Sequence of Exploration Data for Long-Term Dynamic Scene Understanding
by: Huang, Chenguang, et al.
Published: (2024)
by: Huang, Chenguang, et al.
Published: (2024)
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
by: Liu, Shengchao, et al.
Published: (2024)
by: Liu, Shengchao, et al.
Published: (2024)
Reconsidering the energy efficiency of spiking neural networks
by: Yan, Zhanglu, et al.
Published: (2024)
by: Yan, Zhanglu, et al.
Published: (2024)
Similar Items
-
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
by: Zhang, Tianyu, et al.
Published: (2021) -
InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames
by: Li, Haorui, et al.
Published: (2025) -
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
by: Liu, Shengchao, et al.
Published: (2023) -
Flow Along the K-Amplitude for Generative Modeling
by: Du, Weitao, et al.
Published: (2025) -
Gate-level boolean evolutionary geometric attention neural networks
by: Shi, Xianshuai, et al.
Published: (2025)