Robert M. Kirby

发表

Robust topology optimization with low rank approximation using artificial neural networks

Akil Narayan, Vahid Keshavarzzadeh, Robert M. Kirby, 2021, Computational Mechanics.

Optimal allocation of computational resources based on Gaussian process: Application to molecular dynamics simulations

Dmitry Bedrov, Robert M. Kirby, John Chilleri, 2021 .

Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach pdf

Shandian Zhe, Akil Narayan, Robert M. Kirby, 2021, SSRN Electronic Journal.

Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks pdf

Shandian Zhe, Robert M. Kirby, Shibo Li, 2021, NeurIPS.

GP-HMAT: Scalable, ${O}(n\log(n))$ Gaussian Process Regression with Hierarchical Low-Rank Matrices

Shandian Zhe, Akil Narayan, Vahid Keshavarzzadeh, 2021, 2201.00888.

Multifidelity Modeling for Physics-Informed Neural Networks (PINNs) pdf

Shandian Zhe, Akil Narayan, Robert M. Kirby, 2021, Journal of Computational Physics.

Bayesian streaming sparse Tucker decomposition

Shandian Zhe, Shikai Fang, Robert M. Kirby, 2021, UAI.

Characterizing possible failure modes in physics-informed neural networks pdf

Michael W. Mahoney, Shandian Zhe, Amir Gholami, 2021, NeurIPS.

Optimization of Large-Scale Vogel Spiral Arrays of Plasmonic Nanoparticles

Robert M. Kirby, L. Dal Negro, Ren Wang, 2018, Plasmonics.

Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures

Jarom D. Hogue, Robert M. Kirby, A. Narayan, 2022, ArXiv.

Cardiac Position Sensitivity Study in the Electrocardiographic Forward Problem Using Stochastic Collocation and Boundary Element Methods

Robert M. Kirby, J. Stinstra, R. Macleod, 2011, Annals of Biomedical Engineering.

Variational Inference for Nonlinear Inverse Problems via Neural Net Kernels: Comparison to Bayesian Neural Networks, Application to Topology Optimization

Robert M. Kirby, Vahid Keshavarzzadeh, A. Narayan, 2022, 2205.03681.

Infinite-Fidelity Coregionalization for Physical Simulation

Robert M. Kirby, Zheng Wang, Shandian Zhe, 2022, ArXiv.

Decomposing Temporal High-Order Interactions via Latent ODEs

Robert M. Kirby, Shandian Zhe, Shibo Li, 2022, ICML.

Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization

Robert M. Kirby, Richard Baraniuk, S. Osher, 2022, MSML.

Adaptive Self-supervision Algorithms for Physics-informed Neural Networks

Michael W. Mahoney, Robert M. Kirby, A. Gholami, 2022, ECAI.

PROCESSING OF MRI DATA FOR SIMULATION A N D MONITORING OF DRUG DELIVERY

Robert M. Kirby, J. S. Preston, S. Preston, 2012 .

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks

Robert M. Kirby, Shandian Zhe, Shibo Li, 2022, ArXiv.

Batch Multi-Fidelity Active Learning with Budget Constraints

Robert M. Kirby, J. M. Phillips, Shandian Zhe, 2022, NeurIPS.

Infinite-Fidelity Coregionalization for Physical Simulation

Robert M. Kirby, Shandian Zhe, Z. Wang, 2022, NeurIPS.

ENO-Based High-Order Data-Bounded and Constrained Positivity-Preserving Interpolation

Robert M. Kirby, M. Berzins, T. Ouermi, 2022, Numerical Algorithms.

Bayesian Continuous-Time Tucker Decomposition

Robert M. Kirby, Shandian Zhe, Shikai Fang, 2022, ICML.

Variational inference for nonlinear inverse problems via neural net kernels: Comparison to Bayesian neural networks, application to topology optimization

Robert M. Kirby, Vahid Keshavarzzadeh, A. Narayan, 2022, Computer Methods in Applied Mechanics and Engineering.

Sensitivity analysis for an electron transport system: application to the case of wurtzite gallium nitride

Robert M. Kirby, M. Shur, S. O’Leary, 2019, Journal of Computational Electronics.

Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)

Robert M. Kirby, Shandian Zhe, A. Narayan, 2021, J. Comput. Phys..

A metalearning approach for Physics-Informed Neural Networks (PINNs): Application to parameterized PDEs

Robert M. Kirby, Shandian Zhe, A. Narayan, 2021, J. Comput. Phys..

Machine Learning in Heterogeneous Porous Materials

Cedric G. Fraces, Robert M. Kirby, H. Viswanathan, 2022, ArXiv.

Image-BasedMultiresolution Topology Optimization Using Deep Disjunctive Normal ShapeModel

Robert M. Kirby, T. Tasdizen, Vahid Keshavarzzadeh, 2020 .

Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils

Robert M. Kirby, G. Karniadakis, A. Ghoshal, 2023, ArXiv.

Greedy nonlinear autoregression for multifidelity computer models at different scales

W. Xing, M. Razi, R. M. Kirby, 2020 .

A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions

Robert M. Kirby, G. Karniadakis, Ameya Dilip Jagtap, 2023, SSRN Electronic Journal.

AutoIP: A United Framework to Integrate Physics into Gaussian Processes

Michael W. Mahoney, Robert M. Kirby, Shandian Zhe, 2022, ICML.

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks

Robert M. Kirby, Shandian Zhe, Shibo Li, 2022, ICML.

Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures

Jarom D. Hogue, Robert M. Kirby, A. Narayan, 2022, ArXiv.

Multi-Omic Integration of Blood-Based Tumor-Associated Genomic and Lipidomic Profiles Using Machine Learning Models in Metastatic Prostate Cancer.

Robert M. Kirby, Shandian Zhe, M. Kohli, 2023, JCO clinical cancer informatics.

Neural Operator Learning for Ultrasound Tomography Inversion

Robert M. Kirby, S. Joshi, Haocheng Dai, 2023, ArXiv.

International Journal for Numerical Methods in Engineering Analysis and Reduction of Quadrature Errors in the Material Point Method (mpm)

Robert M. Kirby, M. Berzins, Michael Steffen, 2022 .

Convex Optimization-Based Structure-Preserving Filter For Multidimensional Finite Element Simulations

Robert M. Kirby, A. Narayan, Vidhi Zala, 2022, SSRN Electronic Journal.

A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions

Robert M. Kirby, G. Karniadakis, Ameya Dilip Jagtap, 2023, J. Comput. Phys..

Mixed aleatory and epistemic uncertainty quantification using fuzzy set theory

Robert Michael Kirby, Mahsa Mirzargar, Yanyan He, 2015, Int. J. Approx. Reason..

Linköping Studies in Science and Technology. Dissertation, No. 1789 Enhancing Salient Features in Volumetric Data Using Illumination and Transfer Functions Enhancing Salient Features in Volumetric Data Using Illumination and Transfer Functions Jönsson and A. Ynnerman. Correlated Photon Mapping for I

Daniel Jönsson, L. G. Nonato, Robert M. Kirby, 2022 .