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1. Scalable adaptive PDE solvers in arbitrary domains

   Saurabh, K; Ishii, M; Fernando, M; Gao, B; Tan, K; Hsu, M; Krishnamurthy, A; Sundar, H; Ganapathysubramanian, B (January 2021, Super Computing)

   null (Ed.)

   Efficiently and accurately simulating partial differential equations(PDEs) in and around arbitrarily defined geometries, especially with high levels of adaptivity, has significant implications for different application domains. A key bottleneck in the above process is the fast construction of a "good" adaptively-refined mesh. In this work, we present an efficient novel octree-based adaptive discretization approach capable of caring out arbitrarily shaped void regions from the parent domain: an essential requirement for fluid simulations around complex objects. 

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2. SATELLITE DRAG COEFFICIENT MODELING AND ORBIT UNCERTAINTY QUANTIFICATION USING STOCHASTIC MACHINE LEARNING TECHNIQUES

   Paul, S.N.; Sheridan, L.; Mehta, P.; Huzurbazar, S. (January 2021, American Astronomical Society meeting)

   The rapidly increasing congestion in the low Earth environment makes the modeling of uncertainty in atmospheric drag force a critical task, affecting space situational awareness (SSA) activities like the probability of collision estimation. A key element in atmospheric drag modeling is the assessment of uncertainty in the atmospheric drag coefficient estimate. While atmospheric drag coefficients for space objects with known characteristics can be computed numerically, they suffer from large computational costs for practical applications. In this work, we use cost-effective data-driven stochastic methods for modeling the drag coefficients of objects in the low Earth orbit (LEO) region. The training data is generated using the numerical Test Particle Monte Carlo (TPMC) method. TPMC is simulated with Cercignani–Lampis–Lord (CLL) gas-surface interaction (GSI) model. Mehta et al. [1] use a Gaussian process regression (GPR) model to predict satellite drag coefficient, but the authors did not estimate the predictive uncertainty. The first part of this research extends the work by Mehta et al. [1] by fitting a GPR model to the training data and performing predictive uncertainty estimation. The results of the Gaussian fit are then compared against a deep neural network (DNN) model aided by the Monte Carlo dropout approach. To the best of our knowledge, this is the first study to use the aforementioned stochastic deep learning algorithm to perform predictive uncertainty estimation of the estimated satellite drag coefficient. Apart from the accuracy of the models, we also undertake the task of calibrating the models. Simulations are carried out for a spherical satellite followed by the Champ satellite. Finally, quantification of the effect of drag coefficient uncertainty on orbit prediction is carried out for different solar activity and geomagnetic activity levels. 

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3. Delay-Tolerant Data Fusion for Underwater Acoustic Tracking Networks

   https://doi.org/10.1109/UComms50339.2021.9598132  

   Alimadadi, Mohammadreza; Closas, Pau; Stojanovic, Milica (January 2021, Underwater Communications and Networking)

   We consider a network of distributed underwater sensors whose task is to monitor the movement of objects across an area. The sensors measure the strength of signals emanated by the objects and convey the measurements to the local fusion centers. Multiple fusion centers are deployed to cover an arbitrarily large area. The fusion centers communicate with each other to achieve consensus on the estimated locations of the moving objects. We introduce two efficient methods for data fusion of distributed partial estimates when delay in communication is not negligible. We concentrate on the minimum mean squared error (MMSE) global estimator, and evaluate the performance of these fusion methods in the context of multiple-object tracking via extended Kalman filtering. Numerical results show the superior performance compared to the case when delay is ignored. 

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4. A scalable framework for adaptive computational general relativity on heterogeneous clusters

   https://doi.org/10.1145/3330345.3330346  

   Fernando, Milinda; Neilsen, David; Hirschmann, Eric W.; Sundar, Hari (January 2019, Proceedings of the ACM International Conference on Supercomputing ICS'19)

   We present a portable and highly-scalable framework that targets problems in the astrophysics and numerical relativity communities. This framework combines together the parallel Dendro octree with wavelet adaptive multiresolution and an automatic code-generation physics module to solve the Einstein equations of general relativity in the BSSNOK formulation. The goal of this work is to perform advanced, massively parallel numerical simulations of binary black hole and neutron star mergers, including Intermediate Mass Ratio Inspirals (IMRIs) of binary black holes with mass ratios on the order of 100:1. These studies will be used to study waveforms for use in LIGO data analysis and to calibrate approximate methods for generating gravitational waveforms. The key contribution of this work is the development of automatic code generators for computational relativity supporting SIMD vectorization, OpenMP, and CUDA combined with efficient distributed memory adaptive data-structures. These have enabled the development of efficient codes that demonstrate excellent weak scalability up to 131K cores on ORNL's Titan for binary mergers for mass ratios up to 100. 

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5. Manufacturing Feature Recognition With a Sparse Voxel-Based Convolutional Neural Network

   https://doi.org/10.1115/1.4067334  

   Vatandoust, Farzad; Yan, Xiaoliang; Rosen, David; Melkote, Shreyes N (March 2025, Journal of Computing and Information Science in Engineering)

   Abstract Automated manufacturing feature recognition is a crucial link between computer-aided design and manufacturing, facilitating process selection and other downstream tasks in computer-aided process planning. While various methods such as graph-based, rule-based, and neural networks have been proposed for automatic feature recognition, they suffer from poor scalability or computational inefficiency. Recently, voxel-based convolutional neural networks have shown promise in solving these challenges but incur a tradeoff between computational cost and feature resolution. This paper investigates a computationally efficient sparse voxel-based convolutional neural network for manufacturing feature recognition, specifically, an octree-based sparse voxel convolutional neural network. This model is trained on a large-scale manufacturing feature dataset, and its performance is compared to a voxel-based feature recognition model (FeatureNet). The results indicate that the octree-based model yields higher feature recognition accuracy (99.5% on the test dataset) with 44% lower graphics processing unit (GPU) memory consumption than a voxel-based model of comparable resolution. In addition, increasing the resolution of the octree-based model enables recognition of finer manufacturing features. These results indicate that a sparse voxel-based convolutional neural network is a computationally efficient deep learning model for manufacturing feature recognition to enable process planning automation. Moreover, the sparse voxel-based neural network demonstrated comparable performance to a boundary representation-based feature recognition neural network, achieving similar accuracy in single-feature recognition without having access to the exact 3D shape descriptors. 

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