ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
Researchers at ORNL have created a unique simulation technology that allows software systems to participate in slower than real time simulation exercises, and to accomplish this without requiring recompilation of source code, relinking of object files,
The team conducted numerical studies to demonstrate the connection between the parameters of neural networks and the stochastic stability of DMMs.
Researchers from 91°µÍø (ORNL) demonstrated that mode connectivity exists in the loss landscape of parameterized quantum circuits.
Metal Halide Perovskites (MHPs) offer promise for applications in PVs and LEDs due to high device performance and low fabrication cost.
Domain dynamics in polycrystalline materials are explored using a workflow combining deep learning-based segmentation of domain structures with non-linear dimensionality reduction using multilayer rotationally invariant autoencoders (rVAE).
A multi-institutional team of ORNL has utilized the latest computational algorithms and parallelization techniques to enable faster than real-time simulations and applied it to the power system network whose time-domain model represents very large and h
Researchers from ORNL, Stanford University, and Purdue University developed and demonstrated a novel, fully functional quantum local area network (QLAN).