Search Results for author: Shengyu Chen

Found 7 papers, 0 papers with code

Physics-enhanced Neural Operator for Simulating Turbulent Transport

no code implementations31 May 2024 Shengyu Chen, Peyman Givi, Can Zheng, Xiaowei Jia

The precise simulation of turbulent flows is of immense importance in a variety of scientific and engineering fields, including climate science, freshwater science, and the development of energy-efficient manufacturing processes.

FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems

no code implementations17 Nov 2023 Shiyuan Luo, Juntong Ni, Shengyu Chen, Runlong Yu, Yiqun Xie, Licheng Liu, Zhenong Jin, Huaxiu Yao, Xiaowei Jia

This raises a fundamental question in advancing the modeling of environmental ecosystems: how to build a general framework for modeling the complex relationships amongst various environmental data over space and time?

Future prediction

HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation

no code implementations14 Jun 2023 Shengyu Chen, Shihang Feng, Yao Huang, Zhou Lei, Xiaowei Jia, Youzuo Lin, Estaben Rougier

Hybrid Optimization Software Suite (HOSS), which is a combined finite-discrete element method (FDEM), is one of the advanced approaches to simulating high-fidelity fracture and fragmentation processes but the application of pure HOSS simulation is computationally expensive.

Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement

no code implementations24 Apr 2023 Shengyu Chen, Tianshu Bao, Peyman Givi, Can Zheng, Xiaowei Jia

The results on two different types of turbulent flow data confirm the superiority of the proposed method in reconstructing the high-resolution DNS data and preserving the physical characteristics of flow transport.

Super-Resolution

Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature

no code implementations11 Feb 2022 Xiaowei Jia, Shengyu Chen, Yiqun Xie, HaoYu Yang, Alison Appling, Samantha Oliver, Zhe Jiang

However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to downstream river segments.

Physical Simulations

Heterogeneous Stream-reservoir Graph Networks with Data Assimilation

no code implementations11 Oct 2021 Shengyu Chen, Alison Appling, Samantha Oliver, Hayley Corson-Dosch, Jordan Read, Jeffrey Sadler, Jacob Zwart, Xiaowei Jia

In this paper, we propose a heterogeneous recurrent graph model to represent these interacting processes that underlie stream-reservoir networks and improve the prediction of water temperature in all river segments within a network.

Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks

no code implementations6 Sep 2021 Shengyu Chen, Shervin Sammak, Peyman Givi, Joseph P. Yurko1, Xiaowei Jia

Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers.

Super-Resolution Vocal Bursts Intensity Prediction

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