Robust data-driven approach for predicting the configurational energy of high entropy alloys J Zhang, X Liu, S Bi, J Yin, G Zhang, M Eisenbach Materials & Design 185, 108247, 2020 | 63 | 2020 |
Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach X Liu, J Zhang, J Yin, S Bi, M Eisenbach, Y Wang Computational Materials Science 187, 110135, 2021 | 50 | 2021 |
Additive manufacturing and characterization of brittle foams S Bi, E Chen, S Gaitanaros Mechanics of Materials 145, 103368, 2020 | 27 | 2020 |
A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics J Zhang, S Bi, G Zhang Materials & Design, 109213, 2021 | 26 | 2021 |
On the high-temperature crushing of metal foams B Aakash, S Bi, M Shields, S Gaitanaros. HEMI Mach conference, 2019 | 16 | 2019 |
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials S Bi, J Zhang, G Zhang NeurIPS Workshop on Machine Learning for Engineering Modeling, Simulation …, 2020 | 15 | 2020 |
A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models J Zhang, S Bi, G Zhang International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021 | 10 | 2021 |
Atomic structure generation from reconstructing structural fingerprints V Fung, S Jia, J Zhang, S Bi, J Yin, P Ganesh Machine Learning: Science and Technology 3 (4), 045018, 2022 | 8 | 2022 |
Self-supervised anomaly detection via neural autoregressive flows with active learning J Zhang, K Saleeby, T Feldhausen, S Bi, A Plotkowski, D Womble Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2021 | 5 | 2021 |
Blackbox optimization for approximating high-fidelity heat transfer calculations in metal additive manufacturing S Bi, B Stump, J Zhang, Y Lee, J Coleman, M Bement, G Zhang Results in Materials 13, 100258, 2022 | 4 | 2022 |
Chemical complexity in high entropy alloys: a pair-interaction perspective X Liu, J Zhang, S Bi, Y Wang, GM Stocks, M Eisenbach arXiv preprint arXiv:1907.10223, 2019 | 4 | 2019 |
Accelerating inverse learning via intelligent localization with exploratory sampling S Bi, V Fung, J Zhang Proceedings of the AAAI Conference on Artificial Intelligence 37 (12), 14711 …, 2023 | 3 | 2023 |
On the Quantification of Image Reconstruction Uncertainty without Training Data J Zhang, S Bi, V Fung Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 1 | 2024 |
Towards efficient uncertainty estimation in deep learning for robust energy prediction in materials chemistry S Bi, V Fung, J Zhang, G Zhang ICLR 2021 Workshop on Deep Learning for Simulation, 2021 | 1* | 2021 |
A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models S Bi, J Zhang, G Zhang arXiv preprint arXiv:2103.08594, 2021 | 1 | 2021 |
On the Processing, Microstructure and Strength of Brittle Foams S Bi The Johns Hopkins University, 2020 | 1 | 2020 |
On the Quantification of Image Reconstruction Uncertainty without Training Data S Bi, V Fung, J Zhang IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024), 2023 | | 2023 |
Probabilistic Flow-based Models for Semi-Supervised Anomaly Detection and Localization AP Sirui Bi, Kyle Saleeby, Jiaxin Zhang IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022 …, 2022 | | 2022 |
A data-driven approach to study the order-disorder transition in high entropy alloys X Liu, J Zhang, J Yin, S Bi, M Eisenbach, Y Wang Bulletin of the American Physical Society, 2021 | | 2021 |
A Stochastic Approximate Gradient Method for Bayesian Experimental Design S Bi, J Zhang, G Zhang NeurIPS Workshop on Machine Learning and the Physical Sciences, 2020 | | 2020 |