The MS in CSE equips students to translate physics and data into petascale simulations. Core sequences blend parallel algorithms, uncertainty quantification, and scientific ML, while electives tackle quantum materials, aeroacoustics, or epidemiological spread. Students benchmark code on the PACE HPC cluster and collaborate with Oak Ridge leadership-class machines via NSF XSEDE grants.
Exascale CFD solver coupling LES and immersed-boundary methods
Physics-informed neural network for battery degradation forecasting
Co-simulation of power-grid dynamics and traffic EV charging load
Adaptive mesh refinement of tsunami propagation around islands
GPU-accelerated agent-based model of pandemic spread in megacities
Finite-element model of additive-manufactured lattice fatigue
Reduced-order climate model using autoencoder embeddings
Quantum-chemistry workflow optimizing perovskite bandgaps
Uncertainty quantification of hypersonic nose-cone ablation
Parallel graph analytics for genome assembly of rare microbes
Multiphysics simulation of thermo-electric spacecraft power units
AI-guided parameter sweep for polymer rheology viscoelasticity
Stochastic groundwater flow model under sea-level rise scenarios
Hybrid MPI/HPX implementation of seismic wave propagation
Interactive visualization dashboard for billion-cell Monte Carlo runs
Compute the future—from atoms to planets—at massive scale.
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