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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
Posts
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
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Blog Post number 2
Published:
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Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Published:
Short description of portfolio item number 1
Portfolio item number 2
Published:
Short description of portfolio item number 2
publications
Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control
Published in Proceedings of the Royal Society A, 2021
Model-based reinforcement learning (MBRL) is believed to have much higher sample efficiency compared with model-free algorithms by learning a predictive model of the environment. However, the performance of MBRL highly relies on the quality of the learned model, which is usually built in a black-box manner and may have poor predictive accuracy outside of the data distribution. The deficiencies of the learned model may prevent the policy from being fully optimized. Although some uncertainty analysis-based remedies have been proposed to alleviate this issue, model bias still poses a great challenge for MBRL. In this work, we propose to leverage the prior knowledge of underlying physics of the environment, where the governing laws are (partially) known. In particular, we developed a physics-informed MBRL framework, where governing equations and physical constraints are used to inform the model learning and policy search. By incorporating the prior information of the environment, the quality of the learned model can be notably improved, while the required interactions with the environment are significantly reduced, leading to better sample efficiency and learning performance. The effectiveness and merit have been demonstrated over a handful of classic control problems, where the environments are governed by canonical ordinary/partial differential equations.
Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks
Published in Communications Biology, 2023
“PINN with differentiable solver”
[ Paper ]
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
Published in Communications Physics, 2024
“Embedding Finite Difference Operators into Neural Networks in a Multi-Resolution manner”
Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control
Published in Arxiv, 2024
“Asynchronous Parallel Training (APT) strategy for online RL”
[ Paper ]
CoNFiLD: Conditional Neural Field Latent Diffusion Model Generating Spatiotemporal Turbulence
Published in Arxiv, 2024
“Efficient Turbulence generation with Latent Diffusion in Conditional Neural Field encoded space”
[ Paper ]
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.