10 citations · 10 across the 3 of their papers we have counts for
10 papers
AdjointNet: Constraining machine learning models with physics-based codes
Satish Karra, Bulbul Ahmmed, Maruti K. Mudunuru
Physics-informed Machine Learning has recently become attractive for learning physical parameters and features from simulation and observation data. However, most existing methods…
A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing
B. Ahmmed, M. K. Mudunuru, S. Karra +2
Accurate predictions of reactive mixing are critical for many Earth and environmental science problems. To investigate mixing dynamics over time under different scenarios, a high-f…
PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data
B. Ahmmed, M. K. Mudunuru, S. Karra +3
Spectral induced polarization (SIP) is a non-intrusive geophysical method that is widely used to detect sulfide minerals, clay minerals, metallic objects, municipal wastes, hydroca…
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing
M. K. Mudunuru, S. Karra
This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-f…
Large-scale Inversion of Subsurface Flow Using Discrete Adjoint Method
Shu Wang, Satish Karra, Daniel O'Malley
Sensitivity analysis plays an important role in searching for constitutive parameters (e.g. permeability) subsurface flow simulations. The mathematics behind is to solve a dynamic…
Multilevel Graph Partitioning for Three-Dimensional Discrete Fracture Network Flow Simulations
Hayato Ushijima-Mwesigwa, Jeffrey D. Hyman, Aric Hagberg +5
We present a topology-based method for mesh-partitioning in three-dimensional discrete fracture network (DFN) simulations that take advantage of the intrinsic multi-level nature of…