Funded Catalyst Grants | Michigan Institute for Computational Discovery and Engineering

The Michigan Institute for Computational Discovery & Engineering (MICDE) has been funding innovative research in computational science through its Catalyst Grants since 2017.

Funded projects:

2023 Catalyst Grants

An AI coachbot for strengthening self-regulated learning of computational ML and AI

An AI Coachbot for Strengthening Self-Regulated Learning of Computational ML and AI

Von Mises effective stress field distribution of the offshore wind turbine foundation for the “without cone” case subject to ice loading at different time instants

Computational Modeling of a Coupled Aero-Hydro-Structural-Mooring Integrated Dynamic System with Deep Learning for Floating Offshore Wind Turbine Design

Overview of multi-stage diffusion model strategy for improving training and sampling efficiency

Efficient Diffusion Models for Scientific Machine Learning

Foundation Models at Quantum Mechanical Accuracy For Battery Electrolyte Design using Wafer-Scale Computing

Foundation Models at Quantum Mechanical Accuracy For Battery Electrolyte Design using Wafer-Scale Computing

Program Synthesis Tools for Scientific Computing

Program Synthesis Tools for Scientific Computing

Oobleck provably guarantees fault tolerance for up to f failures without sacrificing training throughput by creating pipeline templates that can use all available training resources all the time.

Resilient Distributed Training of Large Models

Simulating the Impact of Early Outbreak Uncertainty on Pandemic Response Intervention Policies

Simulating the Impact of Early Outbreak Uncertainty on Pandemic Response Intervention Policies

2022 Catalyst Grants

Diagram demonstrating computer vision tools

Computer Vision Tools for Automatic Animal Behavioral Classification in Complex Environments

Illustration of model uncertainty estimation

Evidential Crystal-Graph Convolutional Neural Networks for Efficient Global Optimization of Electrocatalysts

Demonstration of hierarchical Tucker decomposition

Multi-scale Continuous Tensor Networks for Quantum Simulations

Occurrence Probability Map of Various Atomic Mechanisms

Probability Mechanisms Map of Dislocation-obstacle Interaction as an Enabler of Physics-based Multiscale Modeling on Precipitation Hardening

2021 Catalyst Grants

computational modeling of cancer cell

Cancer Cells: Greedy Individuals or Team Players?

Computational modeling of household level damage

Computational Modeling of Household Level Damage and Resilience Based on Location Data

Computer chip with binary

Fast Linear Algebra in the Noisy Intermediate-scale Quantum Era

image-based geometric model of a human aorta

Next Generation Computational Tools for Particle-laden Biological Flows in Subject-specific Geometries

clusters from data of human GBM tissue sample

Scalable Inference of Spatially-varying Graphical Models with Applications in Genomics

2020 Catalyst Grants

Pressure and vorticity in a turbulent jet.

Accelerated Computation of Resolvent Modes for High-Dimensional Dynamical Systems

Preliminary data showing that biofilms form complex architectures over time.

Exploring Spatiotemporal Biofilm Development Through the Computational Looking Glass

The project’s “sample-and-dock” approach to generate target-specific screening library for drug targets.

Leveraging Generative Artificial Intelligence for the De Novo Design of Biomolecular Probes

Projected Dark Matter Density Diagram

Uncovering the Origins of the Local Group of Galaxies with Tailored Initial Conditions

2019 Catalyst Grants

Convergence rate of Recovery DG vs. BR2 (the current gold standard) for linear diffusion on a Cartesian mesh. For the same p=2, Recovery achieves 8th order for Recovery, while BR2 is 3rd order.

ALGORITHMIC SOLUTIONS TO MANAGE POWER CONSUMPTION ON EXASCALE SYSTEMS

Milky Way Gaia orbit

DETERMINING THE 3D SHAPE OF MILKY WAY’S DARK MATTER HALO

The DNN model takes in MRI image and predicts the probability of IDH gene mutation

ENABLING TRACTABLE UNCERTAINTY QUANTIFICATION FOR HIGH-DIMENSIONAL PREDICTIVE AI SYSTEMS IN COMPUTATIONAL MEDICINE

Model of evolution of traits/genes

HIERARCHICAL COMPUTING FOR DYNAMIC EVOLUTIONARY INFERENCE OF COMPLEXITY

Long Time Scale Simulations Using Exponential Time Propagators

LONG TIME-SCALE SIMULATIONS USING EXPONENTIAL TIME-PROPAGATORS

Envisaged application in autonomous path planning.

REAL-TIME PHASE-RESOLVED OCEAN WAVE FORECAST WITH DATA ASSIMILATION ENABLED BY GPU-ACCELERATED COMPUTATION

Spatial hotspot of strongly elevated multi-drug resistant (MDR-TB) incidence in Lima, Peru illustrated in red.

SIMULATION-BASED DISCOVERY OF ROBUST ALGORITHMS FOR TARGETING OF INFECTIOUS DISEASE SCREENING AND INTERVENTION

2018 Catalyst Grants

Structure of a block-structured adaptive grid that overlays a so-called ‘cubed-sphere’ base grid.

Advancing the Computational Frontiers of Solution-adaptive, Scale-aware Climate Models

Polar plots showing the rhythmic phases of spikes fired by human neurons

Deciphering the Meaning of Human Brain Rhythms Using Novel Algorithms and Massive, Rare Datasets

Molecular sequence data are input into layers of neural network structures

Deep Learning for Phylogenetic Inference

Bee on flower next to feature space

Embedded Machine Learning Systems to Sense and Understand Pollinator Behavior

classical computer: solves the full problem relatively cheaply and approximately to find the strongly correlated, chemically relevant electrons -> data goes to quantum machine -> quantum computer: solves very accurately an effective small problem only describing strongly correlated electrons -> data goes back to classical machine

Exploring Quantum Embedding Methods for Quantum Computing

Chemical waves and elastic deformations in thin gel sheets.

Teaching Autonomous Soft Machines to Swim

The problem of flood prediction is of high computational complexity.

Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-order Approaches and Uncertainty Quantification

2017 Catalyst Grants

Turbulent Inlet showing most ignition sensitive locations

Black Swans, Dragon Kings and the Science of Rare Events: Problems for the Exascale Era and Beyond

Computational Energy Systems

Aton Labs Neural Activity Patterns

From Spiking Patterns to Memory Formation: Tools for Analysis and Modeling of Network-wide Cognitive Dynamics of the Brain

Vorticity field at a late time in the evolution of an elliptic vortex patch computed by a Lagrangian particle method with remeshing and treecode-accelerated evaluation of the Biot-Savart integral.

Integral Equation Based Methods for Scientific Computing