no code implementations • 9 Mar 2023 • N. V. Jagtap, M. K. Mudunuru, K. B. Nakshatrala
First, the framework is valuable for real-time monitoring of thermal regulatory systems because of rapid forecasting.
1 code implementation • 8 Oct 2021 • M. K. Mudunuru, E. L. D. Cromwell, H. Wang, X. Chen
This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data.
no code implementations • 6 Oct 2021 • M. K. Mudunuru, K. Son, P. Jiang, X. Chen
Our results show that the DL models-based calibration is better than traditional parameter estimation methods, such as generalized likelihood uncertainty estimation (GLUE).
no code implementations • 11 Jan 2021 • N. V. Jagtap, M. K. Mudunuru, K. B. Nakshatrala
Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology.
1 code implementation • 24 Feb 2020 • B. Ahmmed, M. K. Mudunuru, S. Karra, S. C. James, V. V. Vesselinov
The 20 ML emulators based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptron (MLP), are compared to assess these models.
no code implementations • 28 Aug 2019 • M. K. Mudunuru, S. Karra
First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-diffusion equations using a non-negative finite element formulation for different input parameters.
no code implementations • 1 Oct 2018 • B. Yuan, Y. J. Tan, M. K. Mudunuru, O. E. Marcillo, A. A. Delorey, P. M. Roberts, J. D. Webster, C. N. L. Gammans, S. Karra, G. D. Guthrie, P. A. Johnson
We show that the classification accuracy using RF on the filtered data is greater than 90\%. These aspects make the proposed ML framework attractive for event discrimination and signal enhancement under noisy conditions, with strong potential for application to monitoring leaks in $\mathrm{CO}_2$ sequestration.
no code implementations • 30 Jul 2018 • M. K. Mudunuru, N. Panda, S. Karra, G. Srinivasan, V. T. Chau, E. Rougier, A. Hunter, H. S. Viswanathan
In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI).
no code implementations • 5 Jun 2018 • A. Hunter, B. A. Moore, M. K. Mudunuru, V. T. Chau, R. L. Miller, R. B. Tchoua, C. Nyshadham, S. Karra, D. O. Malley, E. Rougier, H. S. Viswanathan, G. Srinivasan
In this paper, five different approaches for reduced-order modeling of brittle fracture in geomaterials, specifically concrete, are presented and compared.
no code implementations • 16 May 2018 • V. V. Vesselinov, M. K. Mudunuru, S. Karra, D. O. Malley, B. S. Alexandrov
An attractive aspect of the proposed ML method is that it ensures the extracted features are non-negative, which are important to obtain a meaningful deconstruction of the mixing processes.
no code implementations • 14 Jun 2016 • M. K. Mudunuru, S. Karra, D. R. Harp, G. D. Guthrie, H. S. Viswanathan
The inputs for ROMs are based on these key sensitive parameters.
no code implementations • 14 Jun 2016 • M. K. Mudunuru, S. Karra, N. Makedonska, T. Chen
In this approach, we first estimate bounds on the statistics for the DFN fracture orientations using microseismic data.