Search Results for author:
Found 12 papers, 3 papers with code
Parametric Encoding with Attention and Convolution Mitigate Spectral Bias of Neural Partial Differential Equation Solvers
Deep neural networks (DNNs) are increasingly used to solve partial differential equations (PDEs) that naturally arise while modeling a wide range of systems and physical phenomena.
Neural Networks with Kernel-Weighted Corrective Residuals for Solving Partial Differential Equations
Physics-informed machine learning (PIML) has emerged as a promising alternative to conventional numerical methods for solving partial differential equations (PDEs).
GP+: A Python Library for Kernel-based learning via Gaussian Processes
In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are completely characterized by their parametric covariance and mean functions.
Multi-fidelity Design of Porous Microstructures for Thermofluidic Applications
We address these challenges by developing a data-driven framework for designing optimal porous microstructures for cooling applications.
On the Effects of Heterogeneous Errors on Multi-fidelity Bayesian Optimization
In this paper, we dispense with these incorrect assumptions by proposing an MF emulation method that (1) learns a noise model for each data source, and (2) enables MFBO to leverage highly biased LF sources which are only locally correlated with the HF source.
Breaking Boundaries: Distributed Domain Decomposition with Scalable Physics-Informed Neural PDE Solvers
Mosaic Flow is a novel domain decomposition method designed to scale physics-informed neural PDE solvers to large domains.
Unsupervised Anomaly Detection via Nonlinear Manifold Learning
Anomalies are samples that significantly deviate from the rest of the data and their detection plays a major role in building machine learning models that can be reliably used in applications such as data-driven design and novelty detection.
Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets
Our NN architecture inversely learns non-trivial (e. g., non-additive and non-hierarchical) biases of the LF sources in an interpretable and visualizable manifold where each data source is encoded via a low-dimensional distribution.
Multi-Fidelity Cost-Aware Bayesian Optimization
Bayesian optimization (BO) is increasingly employed in critical applications such as materials design and drug discovery.
Data Fusion with Latent Map Gaussian Processes
Multi-fidelity modeling and calibration are data fusion tasks that ubiquitously arise in engineering design.
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains
Then, we proposed mosaic flow(MF) predictor, a novel iterative algorithm that assembles the GFNet's inferences for BVPs on large domains with unseen sizes/shapes and BCs while preserving the spatial regularity of the solution.
Latent Map Gaussian Processes for Mixed Variable Metamodeling
In this paper, we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs and are also applicable to mixed data which have both quantitative and qualitative inputs.
