With the advancement of non-Gaussian based variational techniques the need to extend this to hybrid ensemble-variational techniques is the next step towards operational viability. However, the problem lies in the Gaussian assumptions that are made in the derivation of the Kalman filter. In this presentation we shall show a lognormal, a reverse-lognormal, a Gaussian-lognormal, and a Gaussian-reverse-lognormal based Kalman filter and apply them to the Lorenz 1963 model where the z component is known to be have non-Gaussian based errors, we shall compare these new filters with the Extended Kalman Filter. Given these advances in the Kalman filter and the established work with the non-Gaussian variational methods we extend these approaches to create a hybrid non-Gaussian based Maximum Likelihood Ensemble Filter (MLEF)

Steve received his BSc(HONS) in Mathematics and Statistics from the University of Reading in 1998, in 1999 he received his MSc in Numerical Solutions to differential equations (U. Reading), and defended his Ph.D. in December 2003 in Data Assimilation (Higher Order Balance Conditions via Hamiltonian Dynamics for Numerical Weather Prediction) graduating in July 2004. Steve joined CIRA as a NSF Postdoctoral Fellow with Dr. Milija Zupanski working on the Maximum Likelihood Ensemble Filter (MLEF) with Rossby-Haurwitz waves in the CSU shallow water equation model. As part of this work he started to develop the lognormal and mixed Gaussian-lognormal variational assimilation systems. In 2006 he became a RSII working as part of CGAR on 4DVAR in non-Gaussian forms. In January 2011 he became a RSIII working on snow cover assimilation, developing new background error covariances for NRL’s NAVGEM system, as well as extending the non-Gaussian theory to incremental var, retrievals, machine learning to detect distribution change with a series of NSF funded postdocs, as well as recently deriving the non-Gaussian version of the Kalman filter. In 2017 he was elected as a fellow of the Royal Meteorological Society. In 2023 he was promoted to Senior Research Scientist and made a Fellow of CIRA. In February upon Dr. Zupanski’s retirement, Dr. Fletcher became the data assimilation theme lead for CIRA-Fort Collins. He has served as an editor for the AGU journal; Earth and Space Science since August of 2023. He is the author of three textbooks: Semi-Lagrangian advection methods and their applications in geoscience, and two editions of Data Assimilation for the Geosciences: From Theory to Applications.

Ian received a BS in Mathematics from the College of William & Mary in Virginia in 2005. In 2011 he received a PhD in Applied Mathematics from CU-Boulder. He was a postdoc at the Courant Institute of Mathematical Sciences at NYU from 2011 to 2015, and he joined the faculty of Applied Mathematics at CU-Boulder in the fall of 2015, where he has been tenured since 2022. His research interests encompass ocean model development, the development of new data assimilation methods, and the theory of geophysical fluid dynamics and turbulence. I am a lifetime member of both AGU and SIAM. I am a co-chair of the Community Earth System Model (CESM) Ocean Model Working Group, a member of the CLIVAR Ocean Model Development Panel, and an editor for the Journal of Advances in Modeling Earth Systems. In 2025-2026 he became president-elect and in 2027-2028 I will be president of the Nonlinear Geophysics section of AGU.

Pablo holds a PhD in Applied Mathematics and has extensive experience developing fast solvers for linear and nonlinear systems through work across academia and industry in five countries. These systems arise in the solution of partial integral and differential equations, neural network training in AI, and the simulation and optimization of complex processes.

Isabel received her PhD in Mathematics from the University of Nebraska-Lincoln in 2024. Her research focuses on computational fluid dynamics (CFD), regularization methods for nonlinear partial differential equations (PDEs), and geophysical flows. She combines theoretical analysis with high-performance computing to develop and evaluate turbulence models. Her work also includes the study of well-posedness for Navier-Stokes-type equations, along with interests in data assimilation and nonlocal (peridynamic) PDEs.

Computation is used to optimize plasma chambers for semiconductor manufacturing.  This talk will cover the why and how of plasma chambers work semiconductor manufacturing, then consider the parameters that may be varied to obtain optimal performance, where optimal performance is ultimately defined in terms of having the best semiconductor manufacturing outcomes.  At the end of the talk, some of the computational issues are described.

John R. Cary is professor of physics at the University of Colorado at Boulder and a Senior Director and Fellow at Silvaco Group, Inc. He received his PhD from the University of California, Berkeley, in Plasma Physics in 1979. Prof. Cary worked at Los Alamos Nation Laboratory and the Institute for Fusion Studies at the University of Texas, Austin, prior to joining the faculty at the University of Colorado. In 1994, he co-founded Tech‑X Corporation, which concentrates on computational applications for science and engineering and has since been acquired by Silvaco Group, Inc. Prof. Cary has worked in plasma physics, nonlinear dynamics, photonics, beam physics, computational algorithms, and electromagnetics of structures. He is a fellow of the American Physics Society, a recipient of the John Dawson Prize for Numerical Simulation of Plasmas, and a recipient of the NPSS Charles K. Birdsall Award for Contributions to Computational Nuclear and Plasma Sciences. Professor Cary was elected 2018 chair of the Division of Plasma Physics of the APS.

We present the numerical methods, performance portable programming paradigm, verification, and performance of a non-equilibrium plasma fluid solver on adaptive Cartesian grids that can effectively utilize CPU+GPU architectures. Our plasma model solves the governing equations for electron and heavy species transport, electrostatic Poisson and electron temperature on adaptive Cartesian grids. Our solver is written using the AMReX library (Zhang et al., JOSS, 4 (37) 1370, 2019) and can be built and run on widely available vendor specific GPU architectures (NVIDIA/AMD/Intel). We use a non-subcycled second order semi-implicit method for time advance with the composite multi-level multigrid solver from within AMReX for solving the linear equation systems. A python based chemical mechanism parser framework that uses a similar format as CANTERA (Goodwin et al., Zenodo, 2018) yaml files is used to generate unrolled host and device compatible code for plasma chemistry. Non-Cartesian geometry is handled using an immersed boundary approach with cell masking and interface flux corrections. The boundary conditions at the interface cells are cast as Robin conditions and a second-order reconstruction is used to impose Dirichlet (e.g., for Poisson equation) and flux (e.g, electron/ion density and temperature equations) boundary conditions. We describe techniques to overcome challenges associated with linear solver stiffness and geometric resolution when using multi-grid-based methods. Verification of our solver using method of manufactured solutions is presented that indicate formal second order accuracy with central diffusion and fifth order weighted-essentially-non-oscillatory (WENO) advection scheme. We also validate our solver with published literature on low-pressure capacitive and high-pressure streamer discharges and demonstrate its use in 3D simulations of thin-film deposition and etching reactors. Our performance studies indicate 150-400X speed-up using AMD and NVIDIA GPUs per time step compared to a single CPU core for a 4 million cell simulation with 15 species (Sitaraman et al., 2026, Comp. Phys. Comm., 326, 110236, https://github.com/NatLabRockies/vidyut3d ).

Hari Sitaraman is a researcher in the computational science center at NLR. His research is on the development of performance portable solvers and numerical methods for continuum models applied to multiphase, reacting and electrically driven systems

Mesoscale solid mechanics provides a bridge between material microstructure, local deformation and fracture mechanisms, and the effective mechanical response observed in experiments. In this talk, I will present our recent work on phase-field modeling of fracture in microstructure-resolved simulations of inelastic materials.

Resolving steep damage gradients across diffuse cracks in the phase-field modeling of fracture favors the use of high-order finite elements, for which matrix-free methods can provide superior performance and scalability. Here, we implement the Peric & Dettmer constitutive framework for visco-elastoplastic materials in an open-source solid mechanics library supporting matrix-free operators for high-order finite elements with p-multigrid preconditioning on GPUs. We introduce a rheological fracture element arranged in series, such that interactions between inelastic and fracture properties emerge only at homogenization length scales. Numerical simulations of tensile and compressive tests are conducted for synthetic particle-matrix microstructures on an El Capitan high-performance computing prototype. Results are shown to reproduce characteristic inelastic responses and crack propagation patterns.

Fabio Di Gioacchino is a Research Associate at the Center for National Security Initiatives (NSI) at the University of Colorado Boulder, where his research focuses on computational solid mechanics and the development of high-performance finite element methods for modeling deformation and fracture in materials. He received his PhD in Materials Science and Engineering from the University of Manchester and subsequently held postdoctoral research positions at the University of Cambridge and the Colorado School of Mines. His background spans physical metallurgy, experimental micromechanics using high-resolution digital image correlation and electron backscatter diffraction, crystal plasticity, and phase-field modeling of fracture. His current work includes the development of Ratel, an open-source, matrix-free finite element library for solid mechanics designed for large-scale CPU and GPU computing.

Optimization of oil and gas wells is often performed using hydraulic fracture modeling. Demand for large scale field simulations prompts the search for more creative ways to address the issue of computational efficiency. One such possibility is using hydraulic fracture front tracking to boost accuracy of the numerical algorithm, which in turn allows to use a coarser mesh for field-scale simulations without sacrificing accuracy. In particular, the front tracking is based on special partially filled elements that contain the fracture front and allows the fracture geometry to evolve gradually or continuously. This is in contrast to the traditional approach, employed by the vast majority of academic and commercial simulators, in which the fracture geometry is evolved discretely and is determined by the integer number of numerical elements. This talk aims to evaluate and to compare two hydraulic fracture front tracking algorithms, namely the one with Multi Layer Tip Elements (MuLTipEl) and the Implicit Level Set Algorithm (ILSA). The purpose is to better understand the advantages and disadvantages of these approaches with respect to field scale modeling, and to compare their performance with the traditional approach, which is based on the discrete approximation for the fracture geometry.

Egor Dontsov is a Chief Scientist at ResFrac Corporation. Previously, Dr. Dontsov worked at W. D. Von Gonten Laboratories and was an Assistant Professor at the University of Houston. He holds a B.S. degree in Physics from Novosibirsk State University in Russia and a Ph.D. degree in Civil Engineering from the University of Minnesota. Dr. Dontsov has over ten years of academic and industrial experience in hydraulic fracturing and has co-authored over 60 peer-reviewed journal publications and over 20 conference papers, most of which are related to the numerical modeling of hydraulic fracture growth and proppant transport. Dr. Dontsov is a lifetime ARMA and SPE member and currently serves as the Chair of ARMA’s Technical Committee on Hydraulic Fracturing (TCHF) and as a member of ARMA’s Board of Directors.

Atmospheric gravity waves can be excited by just about any fast adjustment processes: deep convection, tropical cyclones, jet streams, flow over topography, volcano eruption, and auroral heating. They become dynamically important, or even dominant, in the Earth’s upper atmosphere and its near space environment. This is because of their omnipresence, their exponential growth with altitudes as the air density decreases, and their contribution to the momentum and energy budgets. They are therefore thought to play a key role in the dynamical coupling of the atmosphere-geospace environment. However, it has been challenging for observations and numerical models alike to quantify their global effects because of the extremely broad scale range and spatial extent. A numerical tool to study the atmosphere-geospace system is the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X), which is one of the atmosphere components of the NCAR’s Community Earth System Model (CESM). One recent effort to address the gravity wave challenge is the development the high-resolution capability of WACCM-X. In this talk I will discuss what we have learned regarding the gravity wave effects on the circulation, transport and composition of the whole atmosphere system. At the same time, our study also reveals the importance of unresolved, smaller scale waves in the global momentum budget. Future progress to better account for the wave effects calls for greater computational power, but also exploitation of physical insights gained from observations and numerical experiments.

Dr. Hanli Liu’s research focus is the theoretical, numerical, and interpretive studies of the dynamics and structure of the Earth's middle and upper atmosphere. He has led the development of the Whole Atmosphere Community Climate Model with thermosphere/ionosphere extension (WACCM-X). He has also worked on the geophysical turbulence and self-organized critical phenomena in geophysics.

Dr. Liu received a B.S. in Fluid Mechanics from the University of Science and Technology of China, and a Ph.D. in Atmospheric and Space Physics from the University of Michigan. He joined High Altitude Observatory of NSF NCAR (National Center for Atmospheric Research) as a postdoctoral fellow in 1997, and continued on as a staff scientist. He is currently a Senior Scientist and the Head of the Geospace Frontiers Section.

The recently launched Helios machine uses 98 high-fidelity trapped-ion qubits with all-to-all connectivity [1]. In this talk I will summarize how the machine operates, and how we benchmark its performance on component and system levels. I will summarize some of the use cases we’ve explored so far in its young life and conclude with an outlook on trapped-ion quantum computing.

[1] Ransford et al., arXiv:2511.05465 (2025)

Dr. Colussi’s work focuses on theoretical aspects of quantum technologies, ranging from atomic, molecular, and optical physics to quantum information and the benchmarking, characterization, and validation of quantum devices.

Dr. Colussi received a PhD. From JILA and the University of Colorado Boulder and a B.A. from Grinnell College in Physics and Mathematics. He worked as a postdoctoral fellow at the Eindhoven University of Technology in the Netherlands and subsequently for the INO-CNR at the Department of Physics at the University of Trento in Italy. He is currently a Senior Advanced Physicist within the Theory Group at Quantinuum.

I will present results on the validation of a three-dimensional particle-in-cell (PIC) solver employing energy-conserving algorithms on unstructured finite element meshes. Both energy-conserving semi-implicit (ECSim) and charge-and-energy-conserving (ChECSim) methods were implemented and tested. ChECSim proved less accurate in plasma-loaded cavity scenarios with strong electromagnetic fields, so validation focuses on the ECSim formulation.

The implementation uses Whitney edge elements with a semi-implicit time-stepping scheme. Validation uses plasma loading in a $1~m^3$ resonant cavity, where the cold plasma dispersion relation $\omega_{2} = \omega_{02} + \omega_{p2}$ provides an analytical benchmark. Density sweeps spanning $\omega_p/\omega_0$ from $0.31$ to $1.75$ demonstrate excellent agreement with theory (within $5$%), with energy conservation below $10^{−7}$ relative error. Mesh convergence confirms less than $2$% difference between coarse and fine discretizations.

Bryon earned a PhD in theoretical nuclear and plasma physics from Duke University in 2009. While at Duke he published multiple articles in the Physical Review on dense plasma systems, and received a Viewpoint recognizing him for excellent research by the American Physical Society. After graduating from Duke, Bryon worked in the Theory Division at Los Alamos National Laboratory, where he was awarded a Director’s Funded Postdoctoral Fellowship. At Los Alamos, he continued to work and publish across a wide array of projects related to hot plasmas and electromagnetic systems. In addition to his technical degrees, Bryon has an MBA from Cornell University, where he graduated in the top 10% of his class.

Bryon joined EMA in 2013. At EMA, Bryon has performed numerical simulation and model development for a number of electromagnetic effects projects, including lightning indirect effects, lightning direct effects, HIRF, and space charging. He developed and executed simulation strategies to help determine the space charging risks for NASA’s Multi-Purpose Crew Vehicle’s FT-1 and EM-1 missions. He has also developed computational tools to bridge near-field, table-top measurement data into a format compatible with EMA3D’s numerical simulation, which can in turn be used to extract the far-field EM emission pattern. He has developed software tools to expand EMA3D’s abilities in the thermal effects of lightning and geometry export for space charging applications.

Outside of work, Bryon enjoys hiking and climbing. He has climbed all of Colorado’s 14,000 foot peaks and more than twenty of Colorado’s 13,000 foot peaks.

Large-scale ocean-bottom node (OBN) arrays of thousands of multi-component instruments deployed over thousands of square kilometers have been successfully employed for active-source seismic exploration at frequencies above 2.0 Hz. The analysis of concurrently recorded lower-frequency ambient wavefield data, however, is only beginning to be systematically explored. A primary objective of such ambient wavefield analyses is to exploit the sensitivity of naturally occurring sub-2.0 Hz energy to construct long-wavelength elastic models that facilitate full waveform inversion (FWI). Achieving this requires a detailed characterization of the ambient wavefield recorded on the seafloor, including the types, frequency content, and effective source distribution of surface-wave modes, the near-seafloor elastic model structure, and the sensitivity of recorded wave modes to subsurface properties.

This talk outlines how prestack ambient seismic data from the Gulf of Mexico, combined with preprocessing and cross-coherence interferometry workflows, enables analysis of ambient wavefields at (ultra)low frequencies (defined here as < 1.0 Hz). Interferometric virtual shot gathers reveal evidence for Scholte, leaky Rayleigh, and guided P-wave mode propagation between 0.001–1.0 Hz. These waves remain coherent to distances of 80 km and show evidence of surface-wave scattering from shallow salt-body structure.

A key advantage of multi-component OBN deployments is the ability to compute the full nine-mode virtual source gather (VSG) volume from cross-correlations of all available channel pairs from the three-component volume — 16-mode with pressure component included. This multicomponent cross-correlation framework can provide substantially richer wavefield information than single-component approaches, enabling robust identification of Scholte and Love-type energy, as well as mixed-mode conversions on off-diagonal components. Dispersion analysis can allow multi-mode phase and group velocity curves to be extracted and inverted for laterally varying Vs models. The processing workflows have been implemented with GPU-parallelized computation to make basin-scale analysis operationally feasible across the full cross-component volume.

Overall, these observations and methodological developments may have important consequences for the early stages of initial model building for elastic FWI analysis of large-scale OBN datasets.

Dr. Aaron Girard is a Research Associate in the Geophysics Department at the Colorado School of Mines (CSM), with an expertise in utilizing continuous seismic recordings and publications in several leading geophysics journals. He completed his undergraduate degree in the CSM Geophysics Department, his MSc in the IDEA-League joint master program at TU Delft, ETH Zürich and RWTH Aachen, his PhD in Geophysics in 2019 at the University of Western Australia and a postdoc in the CSM Geophysics Department. Dr. Girard's research has focused on computational methods for large-scale ambient seismic data analysis on both terrestrial and ocean-bottom node data sets. He is a member of the Center for Wave Phenomena (CWP)