Skip to Main Content U.S. Department of Energy
Energy and Environment Directorate

Himanshu Sharma

(509) 371-7023

PNNL Publications


  • Schram M., K. Rajput, K. Somayaji NS, P. Li, J. St. John, and H. Sharma. 2023. "Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex." Physical Review Accelerators and Beams 26, no. 4:Art. No. 044602. PNNL-SA-190245. doi:10.1103/PhysRevAccelBeams.26.044602
  • Sharma H., L.D. Marinovici, V.A. Adetola, and H.T. Schaef. 2023. "Data-Driven Modeling of Power Generation for a Coal Power Plant Under Cycling." Energy and AI 11. PNNL-SA-171444. doi:10.1016/j.egyai.2022.100214
  • Sharma H., M.B. Shrivastava, and B. Singh. 2023. "Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest." npj Climate and Atmospheric Science 6, no. 2023:2. PNNL-SA-172595. doi:10.1038/s41612-023-00353-y


  • Bhattacharya A., S.S. Vasisht, V.A. Adetola, S. Huang, H. Sharma, and D.L. Vrabie. 2021. "Control Co-Design of Commercial Building Chiller Plant using Bayesian Optimization." Energy and Buildings 246. PNNL-SA-158439. doi:10.1016/j.enbuild.2021.111077
  • Bhattacharya S., H. Sharma, and V.A. Adetola. 2021. "Towards Learning-Based Architectures for Sensor Impact Evaluation in Building Controls." In Proceedings of the Twelfth ACM International Conference on Future Energy Systems (e-Energy '21), June 28-July 2, 2021, Virtual, Online, 493-498. New York, New York:Association for Computing Machinery. PNNL-SA-161319. doi:10.1145/3447555.3466591
  • Sharma H., U. Vaidya, and B. Ganapathysubramanian. 2021. "Contaminant Source Identification from Finite Sensor Data: Perron-Frobenius Operator and Bayesian Inference." Energies 14, no. 20:Art. No. 6729. PNNL-SA-156588. doi:10.3390/en14206729
  • Sharma H., V.A. Adetola, L.D. Marinovici, and H.T. Schaef. 2021. "DATA DRIVEN APPROACH TO ANALYZING THE IMPACT OF POWER PLANT CYCLING ON AIR PREHEATER DEGRADATION AND REMAINING USEFUL LIFE." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition, June 7-11, 2021, Virtual, Online, Volume 9B, Paper No: GT2021-59914, V09BT26A013. New York, New York:ASME. PNNL-SA-158306. doi:10.1115/GT2021-59914

Energy and Environment

Core Research Areas