2025 Energy HPC Conference

Abstract: As we continue to talk about increasing diversity in HPC and the Energy sector, we should also be asking how AI will impact our future work, and how we are preparing our current workforce for advancement.

Questions to consider:
- How do we navigate using AI for work?
- How can we use AI to make our work more productive?
- How are we handling advancement in our companies, from mentoring to rotations to talent development?
- Is AI helping or hurting with advancement?
- How do we account for competing demands of family, elder care, etc.?

Speakers: 

• Cristina Beldica, PhD, MBA - Vice President HPC Software Engineering, Intel Corporation
• Kasia Cevallos - Microsoft 
• Melyssa Fratkin, MBA - Industry Programs Director, Texas Advanced Computing Center (TACC) 
• Arianna Martin - bp
• Rosalinda Mendez, MA - CEO, NOA Research

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Targeting Applications to First-Generation Exascale Systems

Abstract: Aurora and Frontier are the nation’s first exascale computing systems for science and engineering, housed at Argonne and Oak Ridge National Laboratories’ Leadership Computing Facility sites, respectively. From the perspective of managing ALCF’s applications-readiness program for Aurora, the Early Science Program, and working within the Applications Integration component of the Department of Energy’s Exascale Computing Program, I will discuss experiences in developing and optimizing applications for these GPU-accelerated architectures. These applications span a wide range of science and engineering domains and include AI and data-intensive computing components—often together in multicomponent workflows. All of the project teams involved were motivated to maintain portability across both exascale platforms, as well as existing pre-exascale platforms. I will discuss their various chosen implementation means to do this, and some cross-cutting best practices.

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Advancing Reservoir Engineering through High-Performance Computing and Neural Operators on the Cloud

Abstract: Contemporary reservoir engineering applications demand extensive high-fidelity simulations that remain computationally intensive despite advances in high-performance computing. This work presents an integration of scientific machine learning with physics-based reservoir simulation through a scalable, cloud-based workflow utilizing Fourier Neural Operators (FNOs) and GPU-accelerated simulators. FNOs learn mappings between function spaces rather than Euclidean spaces, enabling superior generalization capabilities. The framework is validated using two synthetic 2-phase oil-water systems: a homogeneous case and a heterogeneous case with multi-scale property variations. Results demonstrate that our HPC-enabled FNO implementation achieves approximately 1000x speedup compared to traditional approaches while maintaining acceptable accuracy. Future work will address scaling challenges and enhanced applicability in production environments.

Speakers: 
- Karthik Mukundakrishnan - Director Of Research and Development, Stone Ridge Technology
- Vidyasagar Ananthan - Senior Solutions Architect, Amazon Web Services

Authors: Karthik Mukundakrishnan (Stone Ridge Technology), Vidyasagar Ananthan (Amazon Web
Services), Dan Kahn (Amazon Web Services) and Dmitriy Tishechkin (Amazon Web Services)

Reactive Transport Simulation in Porous Media and Implications for CCS

Abstract: Characterizing the chemical interaction of reactive fluids and the subsurface mineral framework is an essential, but often neglected, component of assessing risk in hydrological, geological, and engineering applications such as enhanced oil recovery (EoR), carbon capture and storage (CCS), and geological hydrogen storage. Reactive transport simulations model the dynamics of fluid flow, solute transport, and chemical interactions in a porous media to address environmental, energy, and resource management challenges. Due to the complexity of the geochemistry and large span of relevant time scales, available simulation tools often grossly simplify the models by solving the flow-transport-reaction equations for reduced systems in terms of chemical, compositional, and spatial representations. Here, we present a novel and scalable simulation framework to directly model the full chemical interaction between fluid and rock, the evolution of the rock mineralogy and porosity, and the complex chemistry of the effluent in the pore-scale of a digitized rock sample. Using the HPC resources available at bp, we demonstrate the capabilities of our software on several rocks and discuss the implications for risk management of CCS projects.

Speakers: 
- Jeremy First, PhD - HPC Computational Scientist, bp
- Yuliana Zapata, PhD - Reservoir Engineer, bp

Jeremy First bio: Computational Scientist in the bp Center for High Performance Computing. He holds a PhD in Physical Chemistry with a portfolio in biophysics from the University of Texas at Austin, and his research at bp includes chemical modeling, digital rocks, and quantum computing.

Yuliana Zapata bio: Reservoir engineer with bp. She is part of the Oil & Gas Technology team working on digital rocks and flow simulation. Yuliana holds a PhD in Petroleum Engineering from the University of Oklahoma, US.

Authors: Jeremy First (bp), Yuliana Zapata (bp), and Srivatsa Mudumba-Ramana (bp/Numerical Algorithms Group)

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Hybrid Quantum/Classical Machine Learning for Molecular Conformation Generation

Abstract: We present an algorithm for hybrid quantum/classical computing environments that generates low-energy conformations of small and medium size hydrocarbon molecules. Despite the importance of conformers in determining physical and chemical properties, traditional physical solvers often struggle to find low-energy conformers due to the large search space. To address this issue, we investigate the potential of using a hybrid generative adversarial network (GAN) algorithm. This algorithm trains a hybrid quantum/classical generator using a simulated photonic quantum processor and a GPU on a dataset of alkane molecules to generate conformers with a specified energy. We find the use of a quantum processor leads to higher-quality results, with the hybrid GAN producing conformers up to 50% closer to the target energy than an equivalent classical GAN.

Speaker: William Clements, PhD - Head of Applications and Software, ORCA Computing

Bio: William Clements is Head of Applications and Software at ORCA Computing, where he develops hybrid quantum/classical algorithms, software and use cases for ORCA's photonic quantum computers. He has co-authored over 20 publications in machine learning and quantum computing and has a physics PhD from the University of Oxford. Before joining ORCA, he worked in roles spanning basic research to product engineering in leading AI deeptech startups.

Authors: William Clements (ORCA Computing), Hugo Wallner (ORCA Computing), Corneliu Buda (bp),
Omar Bacarreza (ORCA Computing), Peter Lemke (bp) and Claudia Perry (bp)

• Moderator: 
  • Addison Snell, Intersect360 Research
• Speakers:
  • Bill Brouwer, SLB
  • Donny Cooper, TotalEnergies
  • Raj Gautam, ExxonMobil
  • Elizabeth L'Heureux, bp

Speaker: Cristel Carolina Brindis Flores - PhD Student, Department of Chemical and Biomolecular Engineering, Rice University
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Poster: Carbon-Neutral Recovery of Natural Gas from Shale: Sequestering CO2 While Enhancing Gas Production
Authors: Cristel Carolina Brindis Flores (Rice University), Walter Chapman (Rice University) and Philip Singer (Rice University)

Speaker: Chen Chen - PhD Student, Department of Civil and Environmental Engineering, Rice University
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Poster: Optimizing US Industrial Heat and Power Systems with Geothermal Deployment in 2030
Authors: Chen Chen (Rice University) and Daniel Cohan (Rice University)

Speaker: Dragana Grbic - PhD Student, Department of Computer Science, Rice University
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Poster: Measuring and Analyzing Application Performance At Exascale
Authors: Dragana Grbic (Rice University) and John Mellor-Crummey (Rice University)

Speaker: Max Hawkins - PhD Student, Department of Computational Science and Engineering, Georgia Institute of Technology
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Poster: Runtime and Energy Analysis of SpMV Hardware Execution Choice
Authors: Max Hawkins (Georgia Institute of Technology), Christian Engman (Georgia Institute of Technology) and Ivan Rocha (Georgia Institute of Technology)

Speaker: Kashif Liaqat - PhD Student, Department of Mechanical Engineering, Rice University
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Poster: Enhancing Data Center Energy Efficiency with a Solar Thermal Boosted Waste Heat Recovery System
Authors: Kashif Liaqat (Rice University) and Laura Schaefer (Rice University)

Speaker: Jason Ludmir - PhD Student, Department of Computer Science, Rice University
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Poster: Unsupervised Quantum Anomaly Detection: Quantum Computing for Detecting Critical Anomalous Events
Authors: Jason Ludmir (Rice University) and Tirthak Patel (Rice University)

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