
We work with researchers throughout Argonne and the scientific and engineering communities to accelerate discovery. Our name reflects three important emphases:
Applied Mathematics
Applied mathematics - which includes computational mathematics, mathematical modeling, numerical analysis, and optimization -- provides a foundation for many scientific and engineering problems. Our research covers the complete spectrum from formulation to algorithm design and analysis to collaboration with domain scientists in solving large-scale problems.
Numerical Software
New technologies are essential to meet the needs of computational scientists. We are dedicated to incorporating new numerical methods from areas such as machine learning, numerical analysis, optimization, and statistics into portable, high-performance, sustainable software and testing it in large-scale simulations of interest to the scientific community.
Statistics
Statistical methods inform all stages of the scientific process, from design of experiments to exploratory analyses, modeling, and inference. We are developing state-of-the-art data-driven statistical methods to achieve new scientific insights, and to produce fast, rigorous methods for artificial intelligence and uncertainty propagation.
Postdoctoral and Student Opportunities
Ways to visit LANS and related groups at Argonne.
Postdoctoral and faculty opportunities:
- Wilkinson Postdoctoral Fellowship in Scientific Computing
- Argonne’s postdoctoral openings and postdoctoral fellowships
- Faculty sabbaticals and other visit programs
Opportunities for domestic and foreign students include:
- Givens Associate program for doctoral students
- General Graduate Research Assistantships
- General undergraduate student internships
LANS Seminar Series
We have a weekly LANS seminar
Publications
View recent publications
LANS People
Staff (cont’d)
Applied Mathematics
- Advancing Integrated Development Environments for Quantum Computing through Fundamental Research (AIDE-QC)
- Center for Efficient Exascale Discretizations
- CFD: Computational Fluid Dynamics
- Co-design Center for Online Data Analysis and Reduction at the Exascale
- Data-Driven Optimization under Uncertainty, Parallel Algorithms and Solver
- Derivative-Free Optimization of Complex Systems
- FASTMath: Frameworks, Algorithms and Scalable Technologies for Mathematics
- Fundamental Algorithmic Research for Quantum Computing (FAR-QC)
- ROMPR: Robust Optimization and Modeling for Phase Retrieval
- SEANO: Structure-Exploiting Algorithms for Nonlinear Optimization
- Computational Differentiation
- Quantum Algorithms, Mathematics and Compilation Tools for Chemical Sciences
Numerical Software
- Block-Structured Adaptive Mesh Refinement Co-Design Center
- Autotuning Compiler Technology for Cross-Architecture Transformation and Code Generation
- Extreme-scale Scientific Software Development Kit for the Exascale Computing Project
- IDEAS-ECP: Advancing Software Productivity for Exascale Applications
- IDEAS: Interoperable Design of Extreme-scale Application Software
- MINOTAUR: Toolkit for Mixed Integer Nonlinear Optimization Problems
- Nek5000: Computational Fluid Dynamics Code
- NekCEM: Nekton for Computational Electromagnetics
- Nuclear Computational Low-Energy Initiative (NUCLEI)
- PETSc: Portable, Extensible Toolkit for Scientific Computation
- TAO: Toolkit for Advanced Optimization
- Community Project for Accelerator Science and Simulation 4
- Optimizing Stochastic Grid Dynamics at Exascale
- Plasma Surface Interactions: Predicting the Performance and Impact of Dynamic PFC Surfaces
- ProVESA: Program Verification for Extreme-Scale Applications
- Simulation of Fission Gas in Uranium Oxide Nuclear Fuel
- Tokamak Disruption Simulation
- Toward Exascale Astrophysics of Mergers and Supernovae (TEAMS)
Statistics
- MACSER: Multifaceted Mathematics for Rare, High-Impact Events in Complex Energy and Environment Systems
- Next-Generation Optimization under Uncertainty: Structure-Oriented Algorithms
- Quantifying Global Structural Errors in Predictive Scientific Simulations
- RAPIDS: A SciDAC Institute for Computer Science and Data
- Accelerating HEP Science: Inference and Machine Learning at Extreme Scales
- HEP Data Analytics on HPC
- HPC Framework for Event Generation at Colliders