Professor of Mathematics, School of Mathematical Sciences, Beihang University
Uncertainty Quantification
Porous Media Analysis
Environment & Energy
I am computational mathematician working on uncertainty quantification. We combines mathematical theory and state-of-the-art computational tools to solve scientific and engineering problems with domain scientists.
Email: wang.peng AT buaa DOT edu DOT cn
2011 🇺🇸 Ph.D in Computational Science
2010 🇺🇸 M.Sc in Engineering Science
University of California, San Diego
2007 🇬🇧 M.Eng 1st honor in Mechanical Engineering
University of Durham
2014–Now Professor of Mathematics
Beihang University
2011–2013 Post Doctorate Research Associate
2013 Staff Scientist
Pacific Northwest National Laboratory
2013 Teaching Fellow
Oundle School
2015–now Editorial Board
International Journal for Uncertainty Quantification
2016–now Board Member
4th Reformation Committee for Scientific Policy of China
Our goal is to develop mathematical tools to quantify uncertainty in nature, engineering systems and our society, and thus provide more accurate and comprehensive predictions of these system states. Applications of our work include environmental protection (flood prediction), ecology (algae blooms), oil recovery and renewable energy systems.
2017-2021 (Co-PI) State Key Research Project, Material Genome Initiative (16,125,000 RMB) —— National Ministry of Science and Technology, China
2016-2019 (PI) General Project, PDF/CDF methods and its numerical scheme for parametric uncertainty quantification (534,000 RMB) —— National Committee of Natural Science Research, China
2015-2018 (PI) Recruitment Program of Global Experts (2,000,000 RMB) —— Organization Department of CPC
2017-2020 (Co-PI) General Project, Uncertainty quantification and diagnosis for software faults (620,000 RMB) —— National Committee of Natural Science Research, China
Introduction to Uncertainty Quantification pdf
* P. Wang and D. Xiu (2018)
China Science Publishing & Media, 2019 (in Chinese),ISBN:9787030594723
Bright-Sun CSD matlab
Bright-Sun: A globally applicable 1-min irradiance clear-sky detection model.
ALKEMIE Matter Studio python
Commerialized, not available.
irradpy: Python package for MERRA-2 download, extraction and usage for clear-sky irradiance modelling, Solar Energy, 2020, vol. 199, pp. 685-693. pdf
* J. Bright*, X. Bai, Y. Zhang, X. Sun, B. Acord and P. Wang (2020)
Solar Energy, 2020, vol. 199, pp. 685-693
Keyword: clear-sky detection, solar irradiance, irradpy
Bright-Sun: A globally applicable 1-min irradiance clear-sky detection model pdf
J. Bright*, X. Sun, C. A. Gueymard, B. Acord, P. Wang* and N. A. Engerer (2020)
Renewable and Sustainable Energy Reviews, 2020, vol. 121
Keyword: bright-sun, clear-sky detection, solar irradiance
Quantification of predictive uncertainty in models of FtsZ ring assembly in Escherichia coli pdf
Y. Ye, A. Ruiz-Martinez, P. Wang* and D. M. Tartakovsky* (2019)
Journal of Theoretical Biology, vol. 484, pp. 110006
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Data assimilation for models with parametric uncertainty pdf
L. Yang, Y. Qin, A. Narayan and P. Wang* (2019)
Journal of Computational Physics, 2019, vol. 396, pp. 785-798.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis pdf
X. Sun, J. Bright*, C. A. Gueymard, B. Acord, P. Wang* and N. Engerer (2019)
Renewable and Sustainable Energy Reviews, 2019, vol. 111, pp. 550-570.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
A new method for an old topic: Efficient and reliable estimation of material bulk modulus pdf
P. Wang*, Y. Qin, M. Cheng, G. Wang, D. Xiu and Z. Sun* (2019)
Computational Materials Science, 2019, vol. 165, pp. 7-12.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
An Efficient Solver for Cumulative Density Function-based Solutions of Uncertain Kinematic Wave Models pdf
M. Cheng#, A. Narayan#, Y. Qin#, P. Wang#*, X. Zhong# and X. Zhu# (2019)
Journal of Computational Physics, 2019, vol. 382, pp.138-151
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Uncertainty quantification on macroscopic properties of random porous media pdf
P. Wang, H. Chen, X. Meng, X. Jiang, D. Xiu and X. Yang* (2018)
Physical Review E, 2018, vol. 98, no. 3, pp. 033306.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
X. Shi, B. Acord, P. Wang* pdf
Incorporating ground measured pollution observations to improve temporally downscaled solar irradiance simulations (2018)
Solar Energy, 2018, vol. 171, 293-301.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Sequential data assimilation with multiple nonlinear models and applications to subsurface flow pdf
L. Yang, A. Narayan and P. Wang* (2017)
Journal of Computational Physics, 2017, vol. 346, pp. 356-368.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Software reliability growth model with temporal correlation in a network environment pdf
J. Xu, S. Yao, S. Yang and P. Wang* (2016)
International Journal for Uncertainty Quantification, 2016, vol. 6, no. 2, pp.141-156.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Uncertainty quantification of scientific proposal evaluations pdf
X. Shi, P. Wang and D. Xiu (2016)
International Journal for Uncertainty Quantification, 2016, vol. 6, no. 2, pp.167-173.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Probabilistic density function method for stochastic ODEs of power systems with uncertain power input pdf
P. Wang, D. A. Barajas-Solano, E. Constantinescu, S. Abhyankar, D. Ghosh, B. F. Smith, Z. Huang and A. M. Tartakovsky* (2015)
SIAM/ASA Journal on Uncertainty Quantification, 2015, vol. 3, no. 1, pp. 873-896.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
PDF method for dynamic system with colored noise pdf
P. Wang*, A. M. Tartakovsky and D. M. Tartakovsky (2013)
Physical Review Letters, 2013, vol. 110, no. 14, pp. 140602.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
CDF solutions of Buckley-Leverett equation with uncertain parameters pdf
P. Wang, D. M. Tartakovsky, K. Jarman Jr. and A. M. Tartakovsky* (2013)
SIAM Journal of multiscale modeling and simulation, 2013, vol. 11, no. 1, pp. 118-133.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Uncertainty quantification in kinematic-wave models pdf
P. Wang and D. M. Tartakovsky* (2012)
Journal of Computational Physics, 2012, vol. 231, no. 23, pp. 7868-7880.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Reduced complexity models for probabilistic forecasting of infiltration rate pdf
P. Wang and D. M. Tartakovsky* (2011)
Advances in Water Resources, 2011, vol. 34, pp. 375-382.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Probabilistic predictions of infiltration into heterogeneous media with uncertain hydraulic parameters pdf
P. Wang and D. M. Tartakovsky* (2011)
Internal Journal for Uncertainty Quantification, 2011, vol. 1, no. 1, pp. 35-47.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface
Effects of spatio-temporal variability of precipitation on contaminant migration in the vadose zone pdf
P. Wang, P. Quinlan and D. M. Tartakovsky* (2009)
Geophysical Research Letters, 2009, vol. 36, L12404.
Keyword: uncertainty quantification, model uncertainty, Kalman filter, particle filter, subsurface