29 citations · 64 across the 9 of their papers we have counts for
10 papers
Goal-Oriented Low-Rank Tensor Decompositions for Numerical Simulation Data
Daniel M. Dunlavy, Eric T. Phipps, Hemanth Kolla +2
We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between t…
An a-posteriori analysis of co-kurtosis PCA based dimensionality reduction using a neural ODE solver
Tadikonda Shiva Sai, Hemanth Kolla, Konduri Aditya
A low-dimensional representation of thermochemical scalars based on cokurtosis principal component analysis (CoK-PCA) has been shown to effectively capture stiff chemical dynamics…
Constrained or Unconstrained? Neural-Network-Based Equation Discovery from Data
Grant Norman, Jacqueline Wentz, Hemanth Kolla +2
Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or accurat…
Accurate Data-Driven Surrogates of Dynamical Systems for Forward Propagation of Uncertainty
Saibal De, Reese E. Jones, Hemanth Kolla
Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for…
Efficient Computation of Tucker Decomposition for Streaming Scientific Data Compression
Saibal De, Zitong Li, Hemanth Kolla +1
The Tucker decomposition, an extension of singular value decomposition for higher-order tensors, is a useful tool in analysis and compression of large-scale scientific data. While…
A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks
Dibyajyoti Nayak, Anirudh Jonnalagadda, Uma Balakrishnan +2
For turbulent reacting flows, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the compu…
A co-kurtosis based dimensionality reduction method for combustion datasets
Anirudh Jonnalagadda, Shubham P. Kulkarni, Akash Rodhiya +2
Principal Component Analysis (PCA) is a dimensionality reduction technique widely used to reduce the computational cost associated with numerical simulations of combustion phenomen…
Randomized Functional Sparse Tucker Tensor for Compression and Fast Visualization of Scientific Data
Prashant Rai, Hemanth Kolla, Lewis Cannada +1
We propose a strategy to compress and store large volumes of scientific data represented on unstructured grids. Approaches utilizing tensor decompositions for data compression have…
Anomaly detection in scientific data using joint statistical moments
Konduri Aditya, Hemanth Kolla, W. Philip Kegelmeyer +3
We propose an anomaly detection method for multi-variate scientific data based on analysis of high-order joint moments. Using kurtosis as a reliable measure of outliers, we suggest…
Achieving algorithmic resilience for temporal integration through spectral deferred corrections
R. W. Grout, H. Kolla, M. L. Minion +1
Spectral deferred corrections (SDC) is an iterative approach for constructing higher- order accurate numerical approximations of ordinary differential equations. SDC starts with an…