General Information
The third North American High Order Methods Conference (NAHOMCon) will be held at Dartmouth College in Hanover New Hampshire. NAHOMCon aims to provide a North American forum for computational scientists, mathematicians, scientists and engineers to share ideas and techniques on, and further the state of the art of, high order methods for the solution of partial differential equations with applications to a broad range of scientific and engineering applications.
The conference will feature six invited talks over a three day period with parallel sections of shorter lectures grouped by topics. We encourage students to register to present research in our planned poster session on June 18. All lectures will be in Kemeny Hall at Dartmouth College (campus maps).
New England Numerical Analysis Day (NENAD) will be held on June 18, 2024, in conjunction with NAHOMCon 2024. A separate registration is required for NENAD participants who are not also attending NAHOMCon 2024. A poster session will take place on June 18, 2024, in the afternoon. NENAD participants are strongly encouraged to present posters.
NAHOMCon 2024 is generously supported by the National Science Foundation and by Dartmouth College. Special thanks also to the Neukom Institute.
Organization of the Conferences
Local organizers:
Anne Gelb (Dartmouth College)
Yoonsang Lee (Dartmouth College)
Yanlai Chen (UMass Dartmouth)
Scientific Committee:
Sigal Gottlieb (UMass Dartmouth)
Thomas Hagstrom (Southern Methodist University)
Gustaaf Jacobs (San Diego State University)
David Kopriva (Florida State University)
Chunlei Liang (Clarkson University)
Catherine Mavriplis (University of Ottawa)
Per-Olof Persson (University of California, Berkeley)
Anders Petersson (Lawrence Livermore National Laboratory)
Tim Warburton (Virginia Tech)
Schedule of the Conference
Day 1, Sunday June 16
| 4:30 PM | Dorms check-in | ||
| 5:00 PM | Welcome Reception | ||
Day 2, Monday June 17
| 8:00 AM | Registration | ||
| 8:30 AM | Welcome | ||
| 8:40 AM | Invited talk (Frank Giraldo, Naval Postgraduate School) | ||
| 9:40AM - 11:10AM | MS: Summation-by-parts: building stable discretizations since 1974 1/2 (Organizers: Jason Hicken of Rensselaer Polytechnic Institute, Jesse Chan of Rice University) | MS: Scientific Machine Learning for Partial Differential Equations 1/2 (Organizers: Zhongqiang Zhang and Qiao Zhuang of Worcester Polytechnic Institute) | |
| Energy-stable and structure-preserving schemes for the stochastic shallow water equations (Akil Narayan, University of Utah) | GPT-PINN and TGPT-PINN: Linear and nonlinear model order reduction toward non-intrusive Meta-learning of parametric PDEs via Physics-Informed Neural Networks (Yanlai Chen, UMass Dartmouth) | ||
| Beyond polynomials: SBP operators for general approximation spaces (Jan Glaubitz, Massachusetts Institute of Technology) | Speeding up and reducing memory usage for scientific machine learning via mixed precision (Lu Lu, Yale University) | ||
| Injected boundary conditions for the compressible Navier-Stokes equations (Anita Gjesteland, University of Waterloo) | A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data (Khemraj Shukla, Brown University) | ||
| Positivity-preserving and entropy-bounded discontinuous Galerkin method for the multicomponent, chemically reacting Navier-Stokes equations (Eric Ching, US Naval Research Laboratory) | Error estimates of residual minimization using neural networks for linear PDEs (Zhongqiang Zhang, Worcester Polytechnic Institute) | ||
| 11:10 AM | Break | ||
| 11:25AM - 12:55PM | MS: Summation-by-parts: building stable discretizations since 1974 2/2 (Organizers: Jason Hicken of Rensselaer Polytechnic Institute, Jesse Chan of Rice) | MS: Scientific Machine Learning for Partial Differential Equations 2/2 (Organizers: Zhongqiang Zhang and Qiao Zhuang of Worcester Polytechnic Institute) | Contributed Talk: Session 1 |
| Shock Capturing Schemes in Discontinuous Galerkin Spectral Element Formulation for Euler and Navier-Stokes Equations (Yulia Peet, Arizona State University) | An artificial viscosity method augmented physics informed neural networks for incompressible flow at high Reynolds number (Zhicheng Wang, Dalian University of Technology) | Spectrally convergent local algorithms for nonlocal problems (Tom Hagstrom, Southern Methodist University) | |
| High order entropy stable discontinuous Galerkin spectral element methods through subcell limiting (Yimin Lin, Rice University) | tLaSDI: Thermodynamics-informed latent space dynamics identification (Yeonjong Shin, North Carolina State University) | A Hermite Method for Nonlinear Dispersive Maxwell’s Equations (Yann-Meing Law, California State University Long Beach) | |
| Fully Discrete Positivity-preserving Entropy Stable Spectral Collocation Schemes for 3-D Navier-Stokes Equations (Wesley Davis, Old Dominion University) | Stabilized hyperbolic PDE solver by adding adaptive localized artificial viscosity to physics-informed neural networks (Ming Zhong, Illinois Institute of Technology) | Graph-theoretic preconditioners for centre-based tissue models (Andreas Buttenschoen, UMass Amherst) | |
| A Fully Discrete Entropy-Stable Flux Reconstruction Scheme using Implicit Temporal Integration for Aerodynamics Applications (Carolyn Pethrik, McGill University) | Two-scale neural networks for partial differential equations with small parameters (Qiao Zhuang, Worcester Polytechnic Institute) | Simple quadrature for near-singular integral operators (Bowei Wu, UMass Lowell) | |
| 12:55 PM | Lunch | ||
| 2:10 PM | Invited talk (Lise-Marie Imbert-Gerard, University of Arizona) | ||
| 3:10PM - 4:40PM | MS: Advances in the Construction and Use of Summation-by-Parts Operators for Time-Dependent PDEs (Organizers: Jan Glaubitz of Massachusetts Institute of Technology, Jan Nordström of Linköping University) | MS: Design and analysis of machine learning algorithms inspired by traditional numerical methods 1/2 (Organizer: Yanlai Chen of UMass Dartmouth) | Contributed Talk: Session 2 |
| On the Stability of the DGSEM for Overset Grid Problems (David Kopriva, Florida State University) | Adaptive reduced-order models for high-speed flow via optimal transport (Robert Loek Van Heyningen, Massachusetts Institute of Technology) | Federated Machine Learning for Experimental Facilities (Rick Archibald, ORNL) | |
| Constructing diagonal-norm summation-by-parts operators on point clouds (Jason E. Hicken, Rensselaer Polytechnic Institute) | Efficient Hybrid Spatio-Temporal Operator Learning (Shuhao Cao, University of Missouri-Kansas City) | Structure-preserving matrix-free finite element method (Svetlana Tokareva, Los Alamos National Laboratory) | |
| Sparse subcell liMassachusetts Institute of Technologying for multi-dimensional SBP operators on general elements (Jesse Chan, Rice University) | Optimal sampling for linear operator learning (Akil Narayan, University of Utah) | Can High-Order Convergence Be Obtained for Practical Problems in Engineering? (Brian Helenbrook, Clarkson University) | |
| Linear and nonlinear boundary conditions for initial boundary value problems: What’s the difference? (Jan Nordström, Linköping University) | Nonlinear scientific computing in machine learning (Wenrui Hao, Pennsylvania State University) | Implementing Immersed Boundaries in Continuous and Discontinuous Spectral Element Methods with hp-Adaptivity (Catherine Mavriplis (and Amik Nayak), University of Ottawa) | |
| Robust and fast differentiation for Sinkhorn optimal transport (Felix Ye, University of Albany) | |||
| Generative Reduced Order Modeling for Parametrized Partial Differential Equations (Ngoc Cuong Nguyen, MIT) | |||
Day 3, Tuesday June 18
| 8:00 AM | Registration (NENAD) | |||
| 8:40 AM | Invited talk (Noel Chalmers, AMD Research) | |||
| 9:40 AM | NENAD Plenary talk (Gil Strang, Massachusetts Institute of Technology) | |||
| 10:30AM - 12:00PM | MS: Advances in Bayesian scientific computing and its applications (Organizers: Tongtong Li of Dartmouth College, Jan Glaubitz of Massachusetts Institute of Technology) | MS: Next-generation solvers using fast and high-order accurate integral equation methods (Organizer: Thomas G. Anderson of Rice University) | Contributed Talk: Session 3 | |
| Edge-preserving methods for spatio-temporal inverse problems (Mirjeta Pasha, Tufts University) | Accelerated Green-function and Fourier-based methods for linear and nonlinear PDE problems in general domains (Oscar Bruno, California Institute of Technology) | Local Adaptivity in Kernel Methods (Jonah A Reeger, Air Force Institute of Technology) | ||
| Structure-preserving data assimilation (Mathieu Le Provost, Massachusetts Institute of Technology) | TBD (David Krantz, KTH Royal Institute of Technology) | High-order time integration for nonlinearly partitioned multiphysics (Ben Southworth, Los Alamos National Lab) | ||
| A novel regularity criterion for the 3D Navier-Stokes equation based on data assimilation (Abhishek Balakrishna, University of Southern California) | High-order density interpolation methods for scattering by inhomogeneous penetrable media with discontinuous material interfaces (Carlos Perez-Arancibia, University of Twente) | A mean-field opinion model and the inference of kernels (Weiqi Chu, University of Massachusetts Amherst) | ||
| Sparse Bayesian inference of biological dynamics (Chenyi Fei, Massachusetts Institute of Technology) | Software integration and numerical analysis of fast, high-order methods for volume integral operators (Thomas G. Anderson, Rice University) | New Analysis of Overlapping Schwarz Methods for Vector Field Problems in Three Dimensions with Generally Shaped Domains (Duk-Soon Oh, Chungnam National University) | ||
| 12:00 PM | Lunch | |||
| 1:20 PM | Invited talk (Scott Field, Umass Dartmouth) | |||
| 2:20PM - 3:50PM | MS: Matrix-free Methods for Advanced High-Fidelity Simulations (Organizer: Yohann Dudouit of Lawrence Livermore National Laboratory) | MS: Design and analysis of machine learning algorithms inspired by traditional numerical methods 2/2 (Organizer: Yanlai Chen of Umass Dartmouth) | Career Panel | |
| Efficient High-Dimension High-Order Matrix-Free Discontinuous Galerkin Methods for High-Fidelity Physics (Yohann Dudouit, Lawrence Livermore National Laboratory) | Low Rank Neural Representation (LRNR) for model reduction of nonlinear conservation laws (Donsub Rim, Washington University in St. Louis) | |||
| GPU optimization of an implicitly time-stepped high-order finite element wave solver (Tim Warburton, Virginia Tech) | Projection method for causal discovery (Yue Yu, Lehigh University) | |||
| Low-Order Preconditioning for SEM-Based Advection-Diffusion Problems (Yu-Hsiang Lan, University of Illinois) | Approximation Rates for Shallow ReLU$^k$ Neural Networks on Sobolev and Besov Spaces (Jonathan Siegel, Texas A&M University) | |||
| Fast coarse grid solvers for Exascale Poisson problems (Thilina Ratnayaka, University of Illinois) | Multifidelity Scientific Machine Learning (Panos Stinis, Pacific Northwest National Laboratory) | |||
| 3:50 PM | Poster Session with Snacks | |||
| 6:00 PM | Conference Banquet | |||
Day 4, Wednesday June 19
| 8:00 AM | |||
| 8:30 AM | |||
| 8:40 AM | Invited talk (Carol Woodward, Lawrence Livermore National Laboratory) | ||
| 9:40AM - 11:10AM | MS Recent Advances in Finite Element Methods for Flow Problems 1/2 (Organizers: Zheng Sun of University of Alabama, Ziyao Xu of University of Notre Dame) | MS: High-order methods for computational relativity (Organizer: Vijay Varma of UMass Dartmouth) | Contributed Talk: Session 4 |
| OEDG: Oscillation-eliminating discontinuous Galerkin method for hyperbolic conservation laws (Zheng Sun, University of Alabama) | Simulating binary black holes with higher order methods using SpECTRE (Geoffrey Lovelace, California State University, Fullerton) | High-order Lagrangian algorithms for Liouville models (Gustaaf Jacobs, San Diego State University) | |
| High-order Runge-Kutta discontinuous Galerkin methods with multi-resolution WENO limiters (Jianxian Qiu, Xiamen University) | Capturing the nonlinear nature of general relativity with Cauchy-characteristic evolution (Keefe Mitman, California Institute of Technology) | A Parallel hp-Adaptive Discontinuous Galerkin Spectral Element Method Solver for Acoustic Problems in Curvilinear Geometries with Dynamic Load Balancing (Catherine Mavriplis, University of Ottawa) | |
| High order entropy stable methods for blood flow simulations (Raven Johnson, Rice University) | Higher order boundary conditions in numerical relativity through Cauchy-characteristic matching (Sizheng Ma, Perimeter Institute) | High-order Active Flux Methods with Gradient degree of freedom (Philip Roe, University of Michigan (emeritus)) | |
| High-order bound-preserving discontinuous Galerkin methods for multicomponent chemically reacting flows (Yang Yang, Michigan Technological University) | Numerical simulations of merging neutron stars with mixed Discontinuous Galerkin / Finite Difference methods (Francois Foucart, University of New Hampshire) | Discontinuous Galerkin Methods for Hypersonic flows (Ngoc Cuong Nguyen, Massachusetts Institute of Technology) | |
| 11:10 AM | Break | ||
| 11:25AM - 12:55PM | MS Recent Advances in Finite Element Methods for Flow Problems 2/2 (Organizers: Zheng Sun of University of Alabama, Ziyao Xu of University of Notre Dame) | MS: High-Accurarcy PDE Solvers through Machine Learning (Organizers: Keenan Eikenberry, Lizuo Liu, Tongtong Li of Dartmouth College) | |
| A Petrov-Galerkin discrete fracture model for fluid flow in fractured media (Ziyao Xu, University of Notre Dame) | Solving High Frequency and Multi-Scale PDEs with Gaussian Processes (Shikai Fang, University of Utah) | ||
| Stochastic energy-based discontinuous Galerkin method for wave equations (Lu Zhang, Rice University) | A Finite Expression Method for Solving High-Dimensional Committor Problems (Zezheng Song, University of Maryland) | ||
| A locking-free and parameter-free enriched Galerkin method of arbitrary order for linear elasticity (Qian Zhang, Michigan Technological University) | Structure preserving and discovery in scientific machine learning (Wei Zhu, University of Massachusetts Amherst) | ||
| Ultra-weak discontinuous Galerkin methods with generalized numerical fluxes for multi-dimensional convection-diffusion and biharmonic equations (Yulong Xing, Ohio State University) | A Causality-DeepONet for Causal Responses of Linear Dynamical Systems (Lizuo Liu, Dartmouth College) | ||
| 12:55 PM | Lunch | ||
| 2:10 PM | Invited talk (Andrew Christlieb, Michigan State University) | ||
| 3:10 PM | Conference End | ||
Financial Support
Students, postdoctoral fellows, and early career professionals may request college dormitory (single) rooms and meal plans when submitting abstracts. The deadline has now passed, and notification of support has gone out.
Invited Speakers
- Noel Chalmers,
AMD Research
- Andrew Christlieb,
Michigan State University
- Scott Field,
University of Massachusetts, Dartmouth
- Frank Giraldo,
Naval Postgraduate School
- Lise-Marie Imbert-Gerard,
University of Arizona
- Gilbert Strang (featured NENAD speaker),
Massachusetts Institute of Technology
- Carol Woodward,
Lawrence Livermore National Laboratory
Registration
Registration: Deadline May 10, 2024
Registration @ NAHOMCon 2024/Nenad 2024
We encourage you to make hotel reservations before then. Questions about registration and housing can be sent to Anne Gelb ().
Support: Some financial support for students, postdoctoral fellows, and early career faculty will be available in the form of local dormitory housing and meal plans. Travel support awards have all been made.
Spam email: Unfortunately there is a phishing email circulating that pretends to be from a travel agency and offers to make travel arrangements to conferences, including NahomCON24. Please do not reply to such emails if you receive them. Any administrative emails pertaining to NahomCON24 will only be sent from a local organizing committee member or a Dartmouth College mathematics department administrator with a dartmouth.edu email address.
Abstract submission
Directions, Air Travel and Shuttle, Accommodations:
Air travel
There is a very small regional airport in Lebanon, NH. If you fly into Boston, there is a bus called the Dartmouth Coach that takes you right into Hanover, across the street from campus. There is also a stop at Lebanon (see accommodations below). Other local airports include Manchester, NH, and Burlington, VT. You will need to rent a car from either location to get to Hanover.
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Advance Transit (Free transit for the Upper Valley)
: Phone # (802) 295-1824. This very convenient free bus service has stops near several hotels throughout the Upper Valley, and includes the towns of White River Junction, Lebanon, and West Lebanon. ·
Hanover Inn
: Phone # 603-643-4300. The Dartmouth Coach (from Boston-Logan airport) drops passengers in front of the hotel. It is across the street from campus, and a five minute walk to Kemeny Hall. ·
Residence
Inn Hanover Lebanon: Phone # 603-643-4511. The DHMC shuttle has transportation to campus. ·
Hilton Garden
Inn Lebanon, NH: Phone # 603-448-3300. The Dartmouth Coach (from Boston-Logan airport) has a stop within walking distance. There is currently no shuttle to Dartmouth. It is a conevenient 10 minute drive for those with cars, and free parking at Dartmouth for conference participants is available.
Driving Directions to
Dartmouth
Nearby accommodations and local transportation
Questions?
Please contact Anne Gelb ().