In addition to the broad scope of papers below, check out our AI/ML Research Spotlight page for selected high-impact publications.

Alkasimi A., Pham A-V., Gardner C., Funsten B. (2023). “Human Activity Recognition Based on 4-Domain Radar Deep Transfer Learning.” Proceedings of the IEEE Radar Conference.

Antoniuk E.R., Cheon G., Wang G., et al. (2023). “Predicting the Synthesizability of Crystalline Inorganic Materials from the Data of Known Material Compositions.” npj Computational Materials.

Bertin N., Zhou F. (2023). “Accelerating Discrete Dislocation Dynamics Simulations with Graph Neural Networks.” Journal of Computational Physics.

Broberg D., Bystrom K., Srivastava S., et al. (2023). “High-Throughput Calculations of Charged Point Defect Properties with Semi-Local Density Functional Theory—Performance Benchmarks for Materials Screening Applications.” npj Computational Materials.

Brown N., Echols B., Zarins J., Grosser T. (2023). “Exploring the Suitability of the Cerebras Wafer Scale Engine for Stencil-Based Computation Codes.” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

Bui V-H., Chang F., Su W., et al. (2023). “Deep Reinforcement Learning-Based Optimal Parameter Design of Power Converters.” 2023 International Conference on Computing, Networking and Communications, ICNC 2023.

Chapline G. (2023). “Quantum Mechanics and Bayesian Machines.” Quantum Mechanics and Bayesian Machines.

Chen Z., Kailkhura B., Zhou Y. (2023). “An Accelerated Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-Level Optimization.” Machine Learning.

Cheung S.W., Choi Y., Copeland D.M., Huynh K. (2023). “Local Lagrangian Reduced-Order Modeling for the Rayleigh-Taylor Instability by Solution Manifold Decomposition.” Journal of Computational Physics.

da Silva F.L., Goncalves A., Nguyen S., et al. (2023). “Language Model-Accelerated Deep Symbolic Optimization.” Neural Computing and Applications.

Glatt R., da Silva F.L., da Costa Bianchi R.A., Costa A.H.R. (2023). “A Study on Efficient Reinforcement Learning Through Knowledge Transfer.” Adaptation, Learning, and Optimization.

Gokhale T., Anirudh R., Thiagarajan J.J., et al. (2023). “Improving Diversity with Adversarially Learned Transformations for Domain Generalization.” Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023.

He X., Choi Y., Fries W.D., et al. (2023). “gLaSDI: Parametric Physics-Informed Greedy Latent Space Dynamics Identification.” Journal of Computational Physics.

Hoang D., Bhatia H., Lindstrom P., Pascucci V. (2023). “Progressive Tree-Based Compression of Large-Scale Particle Data.” IEEE Transactions on Visualization and Computer Graphics.

Huhn Q.A., Tano M.E., Ragusa J.C., Choi Y. (2023). “Parametric Dynamic Mode Decomposition for Reduced Order Modeling.” Journal of Computational Physics.

Ingólfsson H.I., Bhatia H., Aydin F., et al. (2023). “Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.” Journal of Chemical Theory and Computation.

Kadeethum T., Jakeman J.D., et al. (2023). “Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems.” IEEE Access.

Karargyris A., Umeton R., Sheller M.J., et al. (2023). “Federated Benchmarking of Medical Artificial Intelligence with MedPerf.” Nature Machine Intelligence.

Kesavan S.P., Bhatia H., Bhatele A., et al. (2023). “Scalable Comparative Visualization of Ensembles of Call Graphs.” IEEE Transactions on Visualization and Computer Graphics.

Klauber C., Burroughs H., Zhou A. (2023). “Collaborative and Autonomous Black Start: Theory and Implementation.” 2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023.

Kuťák D., Vázquez P-P., Isenberg T., et al. (2023). “State of the Art of Molecular Visualization in Immersive Virtual Environments.” Computer Graphics Forum.

Leventhal S., Gyulassy A., Heimann M., Pascucci V. (2023). “Exploring Classification of Topological Priors with Machine Learning for Feature Extraction.” IEEE Transactions on Visualization and Computer Graphics.

Li S., Lindstrom P., Clyne J. (2023). “Lossy Scientific Data Compression with SPERR.” Proceedings - 2023 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023.

Li Y., Zhao P., Lin X., et al. (2023). “Less is More: Data Pruning for Faster Adversarial Training.” CEUR Workshop Proceedings.

Liu Y., Ponce C., Brunton S.L., Kutz J.N. (2023). “Multiresolution Convolutional Autoencoders.” Journal of Computational Physics.

Paz Soldan Palma J., Gong R., Bocklund B.J., et al. (2023). “Thermodynamic Modeling with Uncertainty Quantification using the Modified Quasichemical Model in Quadruplet Approximation: Implementation into PyCalphad and ESPEI.” Calphad: Computer Coupling of Phase Diagrams and Thermochemistry.

Pearce O., Brink S. (2023). “Finding the Forest in the Trees: Enabling Performance Optimization on Heterogeneous Architectures through Data Science Analysis of Ensemble Performance Data.” International Journal of High Performance Computing Applications.

Subramanyam R., Heimann M., Jayram T.S., et al. (2023). “Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification.” Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023.

Sun H., Shang S-L., Gong R., et al. (2023). “Thermodynamic Modeling of the Nb-Ni System with Uncertainty Quantification using PyCalphad and ESPEI.” Calphad: Computer Coupling of Phase Diagrams and Thermochemistry.

Thopalli K., Anirudh R., Turaga P., Thiagarajan J.J. (2023). “The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation.” IEEE Access.

Trümper L., Ben-Nun T., Schaad P., et al. (2023). “Performance Embeddings: A Similarity-Based Transfer Tuning Approach to Performance Optimization.” Proceedings of the International Conference on Supercomputing.

Tsiokanos I., Tompazi S., Georgakoudis G., et al. (2023). “ARETE: Accurate Error Assessment via Machine Learning-Guided Dynamic-Timing Analysis.” IEEE Transactions on Computers.

Vita J.A., Schwalbe-Koda D. (2023). “Data Efficiency and Extrapolation Trends in Neural Network Interatomic Potentials.” Machine Learning: Science and Technology.

Witman M.D., Goyal A., Ogitsu T., et al. (2023). “Defect Graph Neural Networks for Materials Discovery in High-Temperature Clean-Energy Applications.” Nature Computational Science.

Yang J., Dzanic T., Petersen B., et al. (2023). “Reinforcement Learning for Adaptive Mesh Refinement.” Proceedings of Machine Learning Research.

Yang J., Mittal K., Dzanic T., et al. (2023). “Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement.” Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS.