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![Level Set Learning for Reducing Uncertainties in Function Approximation CSMD Computer Science and Mathematics Division ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-07/level_set_learning_for_reducing_uncertainties_in_function_approximation.png?h=0efe3c03&itok=_gTTu4-8)
ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
![A simulation testbed for building control systems. CSED Computational Sciences and Engineering ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-07/time_managed_virtualization_for_simulating_systems_of_systems.png?h=87cd31ac&itok=LW_wZMM0)
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,
![Phase portraits of DMMs demonstrate the effect of norm bounds on mean fθf (x) and variance fθg (x) networks modeling transition dynamics. The thin lines are samples of stochastic dynamics, whereas the bold lines represent mean trajectories. The colors represent different initial conditions.](/sites/default/files/styles/list_page_thumbnail/public/2022-07/stochastic_stability_of_deep_markov_models.png?h=36d61e15&itok=24-4UpaS)
The team conducted numerical studies to demonstrate the connection between the parameters of neural networks and the stochastic stability of DMMs.
![Mode connectivity in the quantum circuit landscape. CSMD Computational Sciences and Engineering ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-07/mode_connectivity_in_the_quantum_circuit_landscape.png?h=c2278747&itok=g13UhQDV)
Researchers from Oak Ridge National Laboratory (ORNL) demonstrated that mode connectivity exists in the loss landscape of parameterized quantum circuits.
![Machine Learning for Automated Exploration of Metal Halide Perovskites](/sites/default/files/styles/list_page_thumbnail/public/2021-11/Ahmadi_TN%20pic%20260x160.jpg?h=a08abdbb&itok=w_zMf5ch)
Metal Halide Perovskites (MHPs) offer promise for applications in PVs and LEDs due to high device performance and low fabrication cost.
![Disentangling Ferroelectric Wall Dynamics and Identifying Pinning Mechanisms via Deep Learning](/sites/default/files/styles/list_page_thumbnail/public/2021-11/Liu_TN%20260x160.jpg?h=a08abdbb&itok=BotngWsB)
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).
![Illustration of Parallel-in-time algorithm for parallelization of computations. CSED Computational Sciences and Engineering ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-07/towards_faster-than-real-time_simulation_using_parallel-in-time_algorithm_and_high-performance_computing.png?h=ad955db3&itok=YT6wHzG9)
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 Reach Quantum Networking Milestone in Real-World Environment CSED Computational Sciences and Engineering Division ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-07/researchers_reach_quantum_networking_milestone_in_real-world_environment_.png?h=11b0d257&itok=JTeaJ3uf)
Researchers from ORNL, Stanford University, and Purdue University developed and demonstrated a novel, fully functional quantum local area network (QLAN).