Mathematics, and Data
Mathematics, and Data
ACMD focuses on basic computing research that encompasses data and computational engineering, data sciences, high-performance computing, and applied mathematics. Our scientists develop and apply novel methodologies for:
- Software Engineering for Data Integration, Visualization, and Analytics
- Multi-Scale Modeling of Complex Systems
- Uncertainty Quantification
- Graph and Data Analytics
- Autonomous Machine Intelligence
- High-Performance Computing
- Domain-Aware and Physics-Informed Machine Learning
- Co-Design to Advance Computing Frameworks
Scientists and engineers in ACMD apply their expertise in mathematics, algorithms, hardware-software co-design and AI to revolutionize scientific discovery, advance computing systems, and accelerate quantum information science
Computational and Data Engineering Group
Our Computational and Data Engineering group spans the range of mathematical modeling, software development, and data architectures. The group designs, tests, and applies mathematical models to chemical and material structures, creating simulations that improve material construction and performance. In the area of software and data architectures, our scientists and engineers design and manage data flows at scale, develop web portals to access data, create interactive data visualizations, and leverage hybrid data management architectures to create robust and efficient data solutions.
Computational Mathematics Group
Our Computational Mathematics group develops and enables innovative modeling and simulation methodologies that improve the understanding of complex systems. Our core capabilities include multi-scale and multi-fidelity approaches and uncertainty quantification for forward and inverse problems. In collaboration with academia, the group has pioneered the concept of physics-informed, data-driven methods and their application to problems in challenging domains, such as subsurface transport and ice-sheet modeling. The group also leverages its modeling and simulation capabilities to develop digital twins that will guide the design and operation of next-generation energy storage systems.
Data Sciences and Machine Intelligence Group
Our Data Sciences and Machine Intelligence group performs research at the intersection of computer science, data science, and AI. Our capabilities include data architectures, statistical ML, network science, graph analytics, AI, combinatorial optimization, design and control of complex systems, decision sciences, and scalability of methods through high-performance computing. Our scientists work closely with subject matter experts in computational biology, complex infrastructures, high energy physics, computational chemistry, atmospheric sciences, building technologies and the electric power grid, and cyber security, among other domains of national interest.
High-Performance Computing Group
Our High-Performance Computing group conducts fundamental computer science research pertaining to advanced computer architectures; runtime software tools, including compilers, large-scale system performance prediction, and analysis; ML and AI; and sparse tensor methods. Our focus is on hardware/software co-design methodologies to support new computing workloads and advanced accelerator design. Deep expertise in performance modeling methodologies have influenced the design of current and future large-scale computing systems and applications, while expertise in ML and sparse tensor methods are helping to shape the next generation of computational and data science capabilities for PNNL.