Search Results for author: M. K. Mudunuru

Found 12 papers, 2 papers with code

CoolPINNs: A Physics-informed Neural Network Modeling of Active Cooling in Vascular Systems

no code implementations9 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.

Deep Learning to Estimate Permeability using Geophysical Data

1 code implementation8 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.

SWAT Watershed Model Calibration using Deep Learning

no code implementations6 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).

A deep learning modeling framework to capture mixing patterns in reactive-transport systems

no code implementations11 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.

Epidemiology Future prediction

A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing

1 code implementation24 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.

BIG-bench Machine Learning Ensemble Learning +1

Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

no code implementations28 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.

BIG-bench Machine Learning Clustering +1

Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico

no code implementations1 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.

Estimating Failure in Brittle Materials using Graph Theory

no code implementations30 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).

Uncertainty Quantification

Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing

no code implementations16 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.

BIG-bench Machine Learning Clustering

Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

no code implementations14 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.

Clustering

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