Full Waveform Inversion (FWI) is a technique used to visualize and analyze wave propagation through a medium in order to infer its physical properties. This method relies on computational models and algorithms to simulate and interpret the behavior of waves—such as sound, electromagnetic, or seismic waves—as they travel through different materials. By analyzing how these waves are reflected, refracted, or absorbed by the medium, FWI can provide detailed information about the medium’s internal structure, composition, and physical properties, such as density, elasticity, or internal defects. The traditional process typically involves: 1) Wave Simulation: Using physics-based models to simulate how waves propagate through a medium. This may involve solving complex differential equations that describe wave behavior in different contexts. 2) Data Acquisition: Collecting data on wave interactions with the medium using sensors or other measurement devices. This could include data on wave speed, direction, amplitude, and phase changes. 3) Image Reconstruction: Applying computational techniques, such as inverse problems or tomographic reconstruction, to create images or maps of the medium based on the acquired wave data. 4) Analysis: Interpreting the reconstructed images to deduce the physical properties of the medium. This can involve identifying features like boundaries, interfaces, or anomalies within the medium
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[1] Z. Zhou, Y. Lin, Z. Zhang, Y. Wu, Z. Wang, R. Dilmore, and G. Guthrie, A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks, International Journal of Greenhouse Gas Control, Vol 90, 2019.
[2] J. Yang, H. Wang, Y. Sheng, Y. Lin, and L. Yang, A Physics-guided Generative AI Toolkit for Geophysical Monitoring. ArXiv. /abs/2401.03131, 2024 (Accepted to DAC 2024).
[1] Weimin Fu, Shijie Li, Yifang Zhao, Haocheng Ma, Raj Dutta, Xuan Zhang, Kaicheng Yang, Yier Jin, and Xiaolong Guo. “Hardware Phi-1.5B:A Large Language Model Encodes Hardware Domain Specific Knowledge,” 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE/ACM
[2] Weimin Fu, Kaichen Yang, Raj Gautam Dutta, Xiaolong Guo, and Gang Qu. “LLM4SECHW: Leavering domain-specific large language model for hardware debugging,” Asian Hardware Oriented Security and Trust (AsianHOST), 2023.
[3] Weimin Fu, Yifang Zhao, Yier Jin, and Xiaolong Guo. “Enhance Hardware Domain Specific Large Language Model with Reinforcement Learning for Resilience,” The ACM Conference on Computer and Communications Security (CCS) (Accepted as a poster)
[1] Sheng Y, Yang J, Wu Y, Mao K, Shi Y, Hu J, Jiang W, Yang L. The larger the fairer? small neural networks can achieve fairness for edge devices. InProceedings of the 59th ACM/IEEE Design Automation Conference (DAC) 2022 Jul 10 (pp. 163-168).
[2] Y. Sheng, H. Wang, Y. Liu, J. Yang, W Jiang, Y Lin, and L. Yang, APS-USCT: Ultrasound Computed Tomography on Sparse Data via AI-Physic Synergy. ArXiv. /abs/2407.14564, 2024 (Accepted to MICCAI 2024).
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[4] Sheng Y, Yang L, Li J, James J, Xu X, Shi Y, Hu J, Jiang W and Yang L, “Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset”, Accepted by International Conference on Medical Image Computing and Computer Assisted Intervention 2024.
[5] Yang C∗, Sheng Y∗, Dong P∗, Kong Z, Li Y, Yu P, Yang L, Lin X, Wang Y. Fast and Fair Medical AI on the Edge Through Neural Architecture Search for Hybrid Vision Models. In 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) 2023 Oct 28 (pp. 01-09). IEEE.
[6] Sheng Y, Yang J, Jiang W, Yang L. Toward fair and efficient hyperdimensional computing. InProceedings of the 28th Asia and South Pacific Design Automation Conference (ASP-DAC) 2023 Jan 16 (pp. 612-617).
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[1] Jiang, Weiwen, and Lin, Youzuo, “QuGeo: An End-to-end Quantum Learning Framework for Geoscience–A Case Study on Full-Waveform Inversion”, IEEE/ACM DAC 2024.
[2] Li, Jinyang, Zhepeng Wang, Zhirui Hu, Prasanna Date, Ang Li, and Weiwen Jiang. “A novel spatial-temporal variational quantum circuit to enable deep learning on nisq devices.” In 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), vol. 1, pp. 272-282. IEEE, 2023.