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04/06/2023
Neural Operator Learning for Ultrasound Tomography Inversion
Neural operator learning as a means of mapping between complex function ...
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02/28/2023
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
Physics-informed neural networks (PINNs) as a means of solving partial d...
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02/02/2023
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils
Deep neural operators, such as DeepONets, have changed the paradigm in h...
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10/23/2022
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) are emerging as popular mesh-fr...
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08/01/2022
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization
Transformers have achieved remarkable success in sequence modeling and b...
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07/08/2022
Adaptive Self-supervision Algorithms for Physics-informed Neural Networks
Physics-informed neural networks (PINNs) incorporate physical knowledge ...
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07/01/2022
Infinite-Fidelity Coregionalization for Physical Simulation
Multi-fidelity modeling and learning are important in physical simulatio...
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05/07/2022
Variational Inference for Nonlinear Inverse Problems via Neural Net Kernels: Comparison to Bayesian Neural Networks, Application to Topology Optimization
Inverse problems and, in particular, inferring unknown or latent paramet...
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04/13/2022
ENO-Based High-Order Data-Bounded and Constrained Positivity-Preserving Interpolation
A number of key scientific computing applications that are based upon te...
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04/08/2022
Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures
Deep learning using neural networks is an effective technique for genera...
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03/18/2022
Convex Optimization-Based Structure-Preserving Filter For Multidimensional Finite Element Simulations
In simulation sciences, it is desirable to capture the real-world proble...
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12/17/2021
GP-HMAT: Scalable, O(nlog(n)) Gaussian Process Regression with Hierarchical Low-Rank Matrices
A Gaussian process (GP) is a powerful and widely used regression techniq...
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10/26/2021
Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach
Physics-informed neural networks (PINNs) as a means of discretizing part...
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09/02/2021
Characterizing possible failure modes in physics-informed neural networks
Recent work in scientific machine learning has developed so-called physi...
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07/16/2021
Particle Merging-and-Splitting
Robustly handling collisions between individual particles in a large par...
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06/25/2021
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)
Multifidelity simulation methodologies are often used in an attempt to j...
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06/18/2021
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
Bayesian optimization (BO) is a powerful approach for optimizing black-b...
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06/15/2021
Structure-preserving Nonlinear Filtering for Continuous and Discontinuous Galerkin Spectral/hp Element Methods
Finite element simulations have been used to solve various partial diffe...
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04/30/2021
Towards an Extrinsic, CG-XFEM Approach Based on Hierarchical Enrichments for Modeling Progressive Fracture
We propose an extrinsic, continuous-Galerkin (CG), extended finite eleme...
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04/08/2021
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations
Challenges in multi-fidelity modeling relate to accuracy, uncertainty es...
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03/29/2021
A bandit-learning approach to multifidelity approximation
Multifidelity approximation is an important technique in scientific comp...
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03/05/2021
Fast Barycentric-Based Evaluation Over Spectral/hp Elements
As the use of spectral/hp element methods, and high-order finite element...
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01/05/2021
Kernel optimization for Low-Rank Multi-Fidelity Algorithms
One of the major challenges for low-rank multi-fidelity (MF) approaches ...
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12/29/2020
Visualization of topology optimization designs with representative subset selection
An important new trend in additive manufacturing is the use of optimizat...
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12/02/2020
Deep Multi-Fidelity Active Learning of High-dimensional Outputs
Many applications, such as in physical simulation and engineering design...
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09/15/2020
Numerical Testing of a New Positivity-Preserving Interpolation Algorithm
An important component of a number of computational modeling algorithms ...
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08/19/2020
Structure-preserving function approximation via convex optimization
Approximations of functions with finite data often do not respect certai...
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11/27/2019
A Scalable Framework for Solving Fractional Diffusion Equations
The study of fractional order differential operators is receiving renewe...
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10/16/2019
Deep Coregionalization for the Emulation of Spatial-Temporal Fields
Data-driven surrogate models are widely used for applications such as de...
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07/16/2019
The Effect of Data Transformations on Scalar Field Topological Analysis of High-Order FEM Solutions
High-order finite element methods (HO-FEM) are gaining popularity in the...
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06/08/2019
Nektar++: enhancing the capability and application of high-fidelity spectral/hp element methods
Nektar++ is an open-source framework that provides a flexible, high-perf...
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12/30/2018
Allocation strategies for high fidelity models in the multifidelity regime
We propose a novel approach to allocating resources for expensive simula...
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08/30/2018
Parametric Topology Optimization with Multi-Resolution Finite Element Models
We present a methodical procedure for topology optimization under uncert...
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07/08/2018
RBF-LOI: Augmenting Radial Basis Functions (RBFs) with Least Orthogonal Interpolation (LOI) for Solving PDEs on Surfaces
We present a new method for the solution of PDEs on manifolds M⊂R^d of c...
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06/08/2018
Robust Node Generation for Meshfree Discretizations on Irregular Domains and Surfaces
We present a new algorithm for the automatic one-shot generation of scat...
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04/17/2018
Numerical Integration in Multiple Dimensions with Designed Quadrature
We present a systematic computational framework for generating positive ...
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04/10/2018
Curvilinear Mesh Adaptation using Radial Basis Function Interpolation and Smoothing
We present a new iterative technique based on radial basis function (RBF...
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10/08/2017
Exploration of Heterogeneous Data Using Robust Similarity
Heterogeneous data pose serious challenges to data analysis tasks, inclu...
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06/14/2013