Zhongkai Hao

According to our database1, Zhongkai Hao authored at least 38 papers between 2020 and 2026.

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Bibliography

2026
AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training.
CoRR, May, 2026

Discovering Physical Directions in Weight Space: Composing Neural PDE Experts.
CoRR, May, 2026

Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving.
CoRR, March, 2026

Pretrain Finite Element Method: A Pretraining and Warm-start Framework for PDEs via Physics-Informed Neural Operators.
CoRR, January, 2026

2025
An Efficient Graph-Transformer Operator for Learning Physical Dynamics with Manifolds Embedding.
CoRR, December, 2025

A<sup>2</sup>Search: Ambiguity-Aware Question Answering with Reinforcement Learning.
CoRR, October, 2025

AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation.
CoRR, July, 2025

Exploratory Diffusion Policy for Unsupervised Reinforcement Learning.
CoRR, February, 2025

Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

AeroGTO: An Efficient Graph-Transformer Operator for Learning Large-Scale Aerodynamics of 3D Vehicle Geometries.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024
Your Diffusion Model is Secretly a Certifiably Robust Classifier.
CoRR, 2024

Preconditioning for Physics-Informed Neural Networks.
CoRR, 2024

PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Amortized Fourier Neural Operators.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Diffusion Models are Certifiably Robust Classifiers.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Improved Operator Learning by Orthogonal Attention.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

PAPM: A Physics-aware Proxy Model for Process Systems.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Reward Informed Dreamer for Task Generalization in Reinforcement Learning.
CoRR, 2023

Full-Atom Protein Pocket Design via Iterative Refinement.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Reuse Bias in Off-Policy Reinforcement Learning.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks.
Proceedings of the International Conference on Machine Learning, 2023

NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data.
Proceedings of the International Conference on Machine Learning, 2023

GNOT: A General Neural Operator Transformer for Operator Learning.
Proceedings of the International Conference on Machine Learning, 2023

Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Equivariant Energy-Guided SDE for Inverse Molecular Design.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Towards Exploring Large Molecular Space: An Efficient Chemical Genetic Algorithm.
J. Comput. Sci. Technol., 2022

Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications.
CoRR, 2022

A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing.
Proceedings of the International Conference on Machine Learning, 2022

AVT: Au-Assisted Visual Transformer for Facial Expression Recognition.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

2021
A two-stage 3D CNN based learning method for spontaneous micro-expression recognition.
Neurocomputing, 2021

Query-based Adversarial Attacks on Graph with Fake Nodes.
CoRR, 2021

2020
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020


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