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Journal Paper
Y. Gu, C. Han, Y. Chen, and W. W. Xing, “Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, vol. 15, pp. 594–608, 2022, [Link]
W. W. Xing, M. Cheng, K. Cheng, W. Zhang, and P. Wang, “InfPolyn, a Nonparametric Bayesian Characterization for Composition-Dependent Interdiffusion Coefficients,” Materials, vol. 14, no. 13, p. 3635, Jun. 2021 [Link]
H. Wang, C. Li, W. Xing, Y. Ye, and P. Wang, “A machine learning approach to quantify dissolution kinetics of porous media,” Journal of Machine Learning for Modeling and Computing, 2.2, 2021 [Link]
W. I. Ibrahim, M. R. Mohamed, R. M. T. R. Ismail, P. K. Leung, W. W. Xing, and A. A. Shah, “Hydrokinetic energy harnessing technologies: A review,” Energy Reports, vol. 7, pp. 2021–2042, Nov. 2021 [Link]
W. W. Xing, A. A. Shah, P. Wang, S. Zhe, Q. Fu, and R. M. Kirby, “Residual Gaussian process: A tractable nonparametric Bayesian emulator for multi-fidelity simulations,” Applied Mathematical Modelling, vol. 97, pp. 36–56, Sep. 2021
W. W. Xing, R. M. Kirby, and S. Zhe, “Deep coregionalization for the emulation of simulation-based spatial-temporal fields,” Journal of Computational Physics, vol. 428, p. 109984, Mar. 2021
Kerry E. Kelly#, Wei W. Xing#, Tofigh Sayahi, Logan Mitchell, Tom Becnel, Pierre-Emmanuel Gaillardon, Miriah Meyer, and Ross T. Whitaker., “Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes,” Environmental Science & Technology, vol. 55, no. 1, pp. 120–128, Jan. 2021
W. W. Xing, F. Yu, P. K. Leung, X. Li, P. Wang, and A. A. Shah, “A new multi-task learning framework for fuel cell model outputs in high-dimensional spaces,” Journal of Power Sources, vol. 482, p. 228930, Jan. 2021
W. Xing, S. Y. Elhabian, V. Keshavarzzadeh, and R. M. Kirby, “Shared-Gaussian Process: Learning Interpretable Shared Hidden Structure Across Data Spaces for Design Space Analysis and Exploration,” Journal of Mechanical Design, vol. 142, no. 8, Aug. 2020
W. Xing, M. Razi, R. M. Kirby, K. Sun, and A. A. Shah, “Greedy nonlinear autoregression for multifidelity computer models at different scales,” Energy and AI, vol. 1, p. 100012, Aug. 2020
C. Mullen, T. Collins, W. Xing, R. Whitaker, T. Sayahi, T. Becnel, P. Goffin, P. Gaillardon, M. Meyer, K. Kelly, “Patterns of distributive environmental inequity under different PM2.5 air pollution scenarios for Salt Lake County public schools,” Environmental Research, vol. 186, p. 109543, Jul. 2020
D.V. Mallia, A.K. Kochanski, K.E. Kelly, R. Whitaker, W. Xing, L.E. Mitchell, A. Jacques, A. Farguell, J. Mandel, P.E. Gaillardon, and , T. Becnel. “Evaluating Wildfire Smoke Transport Within a Coupled Fire-Atmosphere Model Using a High-Density Observation Network for an Episodic Smoke Event Along Utah’s Wasatch Front,” Journal of Geophysical Research: Atmospheres, vol. 125, no. 20, p. e2020JD032712, 2020
C. Gadd, W. Xing, M. M. Nezhad, and A. A. Shah, “A Surrogate Modelling Approach Based on Nonlinear Dimension Reduction for Uncertainty Quantification in Groundwater Flow Models,” Transport in Porous Media, vol. 126, no. 1, pp. 39–77, Jan. 2019
V. Triantafyllidiis, W. W. Xing, P. K. Leung, A. Rodchanarowan, and A. A. Shah, “Probabilistic sensitivity analysis for multivariate model outputs with applications to Li-ion batteries,” Journal of Physics: Conference Series, vol. 1039, p. 012020, Jun. 2018
A. A. Shah, W. W. Xing, and V. Triantafyllidis, “Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 473, no. 2200, p. 20160809, Apr. 2017
W. W. Xing, V. Triantafyllidis, A. A. Shah, P. B. Nair, and N. Zabaras, “Manifold learning for the emulation of spatial fields from computational models,” Journal of Computational Physics, vol. 326, pp. 666–690, Dec. 2016
W. Xing, A. A. Shah, and P. B. Nair, “Reduced dimensional Gaussian process emulators of parametrized partial differential equations based on Isomap,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 471, no. 2174, p. 20140697, Feb. 2015
Conference Paper
Zheng Wang, Wei Xing, Robert M. Kirby, and Shandian Zhe, “Physics Informed Deep Kernel Learning”, The 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 (to appear)
Shihong Wang, Xueyin Zhang, and Wei W. Xing*, “E-LMC: Extended Linear Model of Coregionalization for Predictions of Spatial Fields”, The 2022 International Joint Conference on Neural Networks (IJCNN) 2022 (to appear)
Shuo Yin, Xiang Jin, Linxu Shi, Kang Wang and Wei W. Xing*, “Efficient Bayesian Yield Analysis and Optimization with Active Learning”, The 59th Design Automation Conference (DAC), 2022 (to appear)
Zhou Jin, Haojie Pei, Yichao Dong, Xiang Jin, Xiao Wu, Wei W. Xing* and Dan Niu*, “Accelerating DC Circuit Simulation with Reinforcement Learning”, The 59th Design Automation Conference (DAC), 2022 (to appear)
Z. Wang, W. Xing, R. Kirby, and S. Zhe, “Multi-Fidelity High-Order Gaussian Processes for Physical Simulation,” in International Conference on Artificial Intelligence and Statistics (AISTAT), Mar. 2021, pp. 847–855 [PDF]
S. Li, W. Xing, R. M. Kirby, and S. Zhe, “Scalable Gaussian Process Regression Networks,” International Joint Conference on Artificial Intelligence (IJCAI), Jul. 2020, vol. 3, pp. 2456–2462 [PDF]
W. Xing, S. Elhabian, R. Kirby, R. T. Whitaker, and S. Zhe, “Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations,” Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 34, no. 04, pp. 6462–6469, Apr. 2020 [PDF]
S. Li, W. Xing, R. Kirby, and S. Zhe, “Multi-Fidelity Bayesian Optimization via Deep Neural Networks,” Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 8521–8531, 2020 [PDF]
S. Zhe, W. Xing, and R. M. Kirby, “Scalable High-Order Gaussian Process Regression,” in The 22nd International Conference on Artificial Intelligence and Statistics (AISTAT), Apr. 2019, pp. 2611–2620 [PDF]
J. Wang#, W. Xing#, R. M. Kirby, and M. Zhang, “Data-Driven Model Order Reduction for Diffeomorphic Image Registration,” in Information Processing in Medical Imaging (IPMI), vol. 11492 [PDF]
W. Xing, A. A. Shah, B. Urasinska-Wojcik, and J. W. Gardner, “Prediction of impurities in hydrogen fuel supplies using a thermally-modulated CMOS gas sensor: Experiments and modelling,” in 2017 IEEE SENSORS, Glasgow, Oct. 2017, pp. 1–3, [Link]
Moore J., Xing W., Wiese J., Dailey M. Becnel T., Goffin P., J., Gaillardon, Kelly K, Butterfield T., Engaging Pre-College Students in Hypothesis Generation using a Citizen Scientist Network of Air Quality Sensors. ASEE Annual Conference and exposition
K. Kelly, W. Xing, P. Goffin, T. Sayahi, T. Becnel, P-E. Gaillardon, A.E. Butterfield, M. Meyer, R. Whitaker, Understanding how pollution episodes affect community-level air quality with a distributed sensor network, AIChE Annual Meeting, Orlando, FL, November 10-15, 2019.
J. Moore, W. Xing, M. Dailey, K. Le, T. Becnel, P. Goffin, M. Meyer, P-E Gaillardon, R.Whitaker, J. Wiese, A.E. Butterfield, K.E. Kelly, Engaging middle and high school students in hypothesis generation using a citizen-scientist network of air quality sensors, AIChE Annual Meeting, Orlando, FL, November 10-15, 2019.
T. Sayahi, P.-E. Gaillardon, R. Whitaker, M. Meyer, T. Butterfield, P. Goffin, T. Becnel, A. Biglari, D. Kaufman, T. Sayahi, W. Xing, K. Kelly, Platform: Long-term evaluation of the plantower PMS sensor. 10th International Aerosol Conference, September 2nd-7th, St. Louis, Mo, 2018.
K.E. Kelly, P.-E. Gaillardon, R. Whitaker, M. Meyer, T. Butterfield, P. Goffin, T. Becnel, A. Biglari, D. Kaufman, T. Sayahi, W. Xing, Poster: A layered framework for integrating low-cost sensor data and for engaging citizens to understand PM2.5 exposure. 10th International Aerosol Conference, September 2nd-7th, St. Louis, Mo, 2018.
Triantafyllidis, V., Xing, W., Shah, A. A., & Nair, P. B. (2016). Neural network emulation of spatio-temporal data using linear and nonlinear dimensionality reduction. In Advanced Computer and Communication Engineering Technology (pp. 1015-1029). Springer, Cham.
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