Mathematical Modeling, Simulation and Analysis
Emphasis is on engineering solutions for theory-driven, physics-based, physiologically realistic, virtual representations of biomedical systems, with a particular weight on multiscale modeling. NIBIB interests include, but are not limited to:
- multiscale modeling methodologies to bridge spatial and temporal scales
- predictive modeling frameworks to facilitate the formation of testable hypothesis
- non-standard methodologies that address modeling challenges, such as, uncertainty quantification, modularity, sparse data, scaling of species models, etc.
- methods to address model credibility, reproducibility, and reuse by the biomedical community
Emphasis is on engineering, mathematical, statistical and computational approaches for emulating system dynamics and processes implicated in biomedical applications, with a particular weight on medical simulator design and development to reduce medical errors and increase patient safety. NIBIB interests include, but are not limited to:
- virtual coaches incorporating artificial intelligence for performance training in medical procedures and workflows to provide real-time feedback to the end-user
- simulation interfaces to facilitate dissemination and use of virtual environments for end-users
- realistic representations of anatomy, tissue, instrument, tactile feedback, and collision dynamics
- simulator designs that focus on complicated or rare procedures, or rare adverse events
- simulators that replicate “real life” workflows, including planning, warm-up exercises, and rehearsal leading up to the actual procedure
- portable, easy-to-use simulators for skilled practitioners in rural and low-resource settings
Emphasis is on theoretical, mathematical, statistical and engineering approaches to interpret the behavioral of complex biomedical data and its dynamics, with a particular weight on paradigm-shifting methodologies and software interfaces. NIBIB interests include, but are not limited to:
- intelligent control systems approaches of medical devices and hardware systems
- novel methods to extract fundamental dynamical (mechanistic) features and patterns from large nonlinear, spatio-temporal datasets for real-time data analysis
- novel implementations of dynamic versions of principal component analysis
- tools to address data dimensionality, data fusion, and data assimilation methods to combine heterogeneous data and link sparse data with mechanisms
- formal statistical inference frameworks to conduct network connectivity and causal-inference analyses from different types of biomedical data
The NIH BRAIN Initiative supports many projects in this program.
Applications proposing to use, rather than develop Mathematical Modeling, Simulation and Analysis tools should be referred to another institute.