Velimir V. Vesselinov

Publication Activity (10 Years)

Years Active: 2012-2023
Publications (10 Years): 15

Top Topics

Machine Learning

Variational Bayesian

Decision Analysis

Top Venues

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Stat. Anal. Data Min.

Publications

Daniel O'Malley, Sarah Y. Greer, Aleksandra Pachalieva, Wu Hao, Dylan Robert Harp, Velimir V. Vesselinov
DPFEHM: a differentiable subsurface physics simulator. J. Open Source Softw. 8 (90) (2023)
Maruti Kumar Mudunuru, Velimir V. Vesselinov, Bulbul Ahmmed
GeoThermalCloud: Machine Learning for Geothermal Resource Exploration. CoRR (2022)
B. Ahmmed, Maruti Kumar Mudunuru, Satish Karra, S. C. James, Velimir V. Vesselinov
A comparative study of machine learning models for predicting the state of reactive mixing. J. Comput. Phys. 432 (2021)
Maruti Kumar Mudunuru, Daniel O'Malley, Shriram Srinivasan, Jeffrey D. Hyman, Matthew R. Sweeney, Luke Frash, Bill Carey, Michael R. Gross, Nathan J. Welch, Satish Karra, Velimir V. Vesselinov, Qinjun Kang, Hongwu Xu, Rajesh J. Pawar, Tim Carr, Liwei Li, George D. Guthrie, Hari S. Viswanathan
Physics-Informed Machine Learning for Real-time Reservoir Management. AAAI Spring Symposium: MLPS (2020)
B. Ahmmed, Maruti Kumar Mudunuru, Satish Karra, S. C. James, Velimir V. Vesselinov
A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing. CoRR (2020)
Boian S. Alexandrov, Valentin G. Stanev, Velimir V. Vesselinov, Kim Ø. Rasmussen
Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers. Stat. Anal. Data Min. 12 (4) (2019)
Velimir V. Vesselinov, Maruti Kumar Mudunuru, Satish Karra, Dan O'Malley, Boian S. Alexandrov
Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing. J. Comput. Phys. 395 (2019)
Daniel O'Malley, John K. Golden, Velimir V. Vesselinov
Learning to regularize with a variational autoencoder for hydrologic inverse analysis. CoRR (2019)
Zhiming Lu, Velimir V. Vesselinov, Hongzhuan Lei
Identifying arbitrary parameter zonation using multiple level set functions. J. Comput. Phys. 364 (2018)
Velimir V. Vesselinov, Maruti Kumar Mudunuru, Satish Karra, Dan O'Malley, Boian S. Alexandrov
Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing. CoRR (2018)
Elizabeth Qian, Benjamin Peherstorfer, Daniel O'Malley, Velimir V. Vesselinov, Karen Willcox
Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices. SIAM/ASA J. Uncertain. Quantification 6 (2) (2018)
Daniel O'Malley, Velimir V. Vesselinov, Boian S. Alexandrov, Ludmil B. Alexandrov
Nonnegative/binary matrix factorization with a D-Wave quantum annealer. CoRR (2017)
Valentin G. Stanev, Filip L. Iliev, Velimir V. Vesselinov, Boian S. Alexandrov
Machine learning approach for identification of release sources in advection-diffusion systems. CoRR (2016)
Daniel O'Malley, Velimir V. Vesselinov
ToQ.jl: A high-level programming language for D-Wave machines based on Julia. HPEC (2016)
Filip L. Iliev, Valentin G. Stanev, Velimir V. Vesselinov, Boian S. Alexandrov
Sources identification using shifted non-negative matrix factorization combined with semi-supervised clustering. CoRR (2016)
Daniel O'Malley, Velimir V. Vesselinov
A Combined Probabilistic/Nonprobabilistic Decision Analysis for Contaminant Remediation. SIAM/ASA J. Uncertain. Quantification 2 (1) (2014)
Vicky L. Freedman, Xingyuan Chen, Stefan Finsterle, Mark D. Freshley, Ian Gorton, Luke J. Gosink, Elizabeth H. Keating, Carina Lansing, William A. M. Moeglein, Christopher J. Murray, George Shu Heng Pau, Ellen Porter, Sumit Purohit, Mark Rockhold, Karen Schuchardt, Chandrika Sivaramakrishnan, Velimir V. Vesselinov, Scott R. Waichler
A high-performance workflow system for subsurface simulation. Environ. Model. Softw. 55 (2014)
Velimir V. Vesselinov, Dylan R. Harp
Adaptive hybrid optimization strategy for calibration and parameter estimation of physical process models. Comput. Geosci. 49 (2012)
Dylan R. Harp, Velimir V. Vesselinov
An agent-based approach to global uncertainty and sensitivity analysis. Comput. Geosci. 40 (2012)