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Mudunuru, Maruti

Publications and source records attributed to Mudunuru, Maruti.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Complementing the CCS Class VI Well Permit Process with DOE-NETL's SMART Initiative Tools and Workflows

This is a presentation on model explorer developed under SMART initiative Task 2. Our team will present the current progress of the model explorer in using machine learning models to accelerate CCS project at GWPC meeting. Model explorer bring new capabilities, (fast, Realtime, and accurate) that can help CCS stakeholders including regulatory agencies, public and site operators make faster decisions and process information and data.

Hosseini, Seyyed↗
DOE OSTI2024

Pore2Chip

Pore2Chip is a Python module designed to streamline the process of analyzing X-ray computed tomography (XCT) images of soil and creating 2D micromodel designs based on that analysis.

Mudunuru, Maruti↗
DOE OSTI2023

Perspectives on Artificial Intelligence for Predictions in Ecohydrology

Abstract In November 2021, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held, which involved hundreds of researchers from dozens of institutions. There were 17 sessions held at the workshop, including one on ecohydrology. The ecohydrology session included various breakout rooms that addressed specific topics, including 1) soils and belowground areas; 2) watersheds; 3) hydrology; 4) ecophysiology and plant hydraulics; 5) ecology; 6) extremes, disturbance and fire, and land-use and land-cover change; and 7) uncertainty quantification methods and techniques. In this paper, we investigate and report on the potential application of artificial intelligence and machine learning in ecohydrology, highlight outcomes of the ecohydrology session at the AI4ESP workshop, and provide visionary perspectives for future research in this area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Emergent Concepts from a Community Ideation on AI4ESP

This AI4ESP white paper is the outcome of an open ideation session led by PNNL that numerous institutions participated in. The leadership team included Amy Goldman, Huiying Ren, Tim Scheibe, and James Stegen. The white paper is broken into two sections: (1) A summary of the ideation session, and (2) a short summary of a ‘meta idea’ that emerged from combining multiple ideas spanning the three primary focal area themes articulated in BER’s AI4ESP guidance: A - Data Acquisition and Assimilation, B - Predictive Modeling, and C - Insight from Complex Data. The white paper, therefore, has two goals. The first section is meant to be a resource for innovating around how to pursue open, community-based ideation in virtual environments. The second section is meant as an example of the kinds of ‘meta ideas’ that can emerge from bringing diverse ideas together in a single shared space. We consider the second section to be a submission to BER’s request for white papers around AI4ESP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Towards Trustworthy and Interpretable Deep Learning-assisted Ecohydrological Models

The transformational science question we plan to address is: How do we leverage in-situ observations and simulations from process-based ecohydrological model to construct interpretable and trustworthy deep learning (DL) models for improved reliability of prediction of quantities of interest (QoIs) under hydro-climatic extremes?

54 ENVIRONMENTAL SCIENCES↗