Ye Lu

• PhD
• Assistant professor at University of Maryland, Baltimore County

A data-free, predictive scientific AI model, Tensor-decomposition-based A Priori Surrogate (TAPS), is proposed for tackling ultra large-scale engineering simulations with significant speedup, memory savings, and storage gain. TAPS can effectively obtain surrogate models for high-dimensional parametric problems with equivalent zetta-scale ($10^{21}$...

This paper presents a convolution tensor decomposition based model reduction method for solving the Allen-Cahn equation. The Allen-Cahn equation is usually used to characterize phase separation or the motion of anti-phase boundaries in materials. Its solution is time-consuming when high-resolution meshes and large time scale integration are involve...

Challenge 3 of the 2022 NIST additive manufacturing benchmark (AM Bench) experiments asked modelers to submit predictions for solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for single and multiple track laser powder bed fusion process using moving lasers. An in-house developed A dditive M anufacturing C omputationa...

This paper introduces an extended tensor decomposition (XTD) method for model reduction. The proposed method is based on a sparse non-separated enrichment to the conventional tensor decomposition, which is expected to improve the approximation accuracy and the reducibility (compressibility) in highly nonlinear and singular cases. The proposed XTD m...

We propose the Convolution Hierarchical Deep-learning Neural Network (C-HiDeNN) that can be tuned to have superior accuracy, higher smoothness, and faster convergence rates like higher order finite element methods (FEM) while using only linear element’s degrees of freedom. This is based on our newly developed convolution interpolation theory (Lu et...

This paper presents a general Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN) computational framework for solving partial differential equations. This is the first paper of a series of papers devoted to C-HiDeNN. We focus on the theoretical foundation and formulation of the method. The C-HiDeNN framework provides a flexible way to...

High-resolution structural topology optimization is extremely challenging due to a large number of degrees of freedom (DoFs). In this work, a Convolution-Hierarchical Deep Learning Neural Network-Tensor Decomposition (C-HiDeNN-TD) framework is introduced and applied to solve the computationally challenging giga-scale topology optimization problem u...

The hierarchical deep-learning neural network (HiDeNN) (Zhang et al. Computational Mechanics, 67:207–230) provides a systematic approach to constructing numerical approximations that can be incorporated into a wide variety of Partial differential equations (PDE) and/or Ordinary differential equations (ODE) solvers. This paper presents a framework o...

Challenge 3 of the 2022 NIST additive manufacturing benchmark (AM-Bench) experiments asked modelers to submit predictions for solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for single and multiple track laser powder bed fusion process using moving lasers. An in-house developed Additive Manufacturing Computational F...

Deterministic computational modeling of laser powder bed fusion (LPBF) process fails to capture irregularities and roughness of the scan track, unless expensive powder-scale analysis is used. In this work we developed a stochastic computational modeling framework based on Markov Chain Monte Carlo (MCMC) capable of capturing the irregularities of LP...

This paper presents a tensor decomposition (TD) based reduced-order model of the hierarchical deep-learning neural networks (HiDeNN). The proposed HiDeNN-TD method keeps advantages of both HiDeNN and TD methods. The automatic mesh adaptivity makes the HiDeNN-TD more accurate than the finite element method (FEM) and conventional proper generalized d...

This paper presents the concept of reduced order machine learning finite element (FE) method. In particular, we propose an example of such method, the proper generalized decomposition (PGD) reduced hierarchical deep-learning neural networks (HiDeNN), called HiDeNN-PGD. We described first the HiDeNN interface seamlessly with the current commercial a...

Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of the parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the partici...

Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms...

In the Air Force Research Laboratory Additive Manufacturing Challenge Series, melted track geometries for a laser powder bed fusion (L-PBF) process of Inconel 625 were used to challenge and validate computational models predicting melting and solidification behavior. The impact of process parameters upon single-track single-layer, multi-track singl...

This paper presents a proper generalized decomposition (PGD) based reduced-order model of hierarchical deep-learning neural networks (HiDeNN). The proposed HiDeNN-PGD method keeps both advantages of HiDeNN and PGD methods. The automatic mesh adaptivity makes the HiDeNN-PGD more accurate than the finite element method (FEM) and conventional PGD, usi...

Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series asks the participants to predict the grain-average elastic strain tensors of a few specific challenge grains during tensile loading, based on experimental data and extensive characterization of an IN625 test specimen. In this article, we present our st...

Thermal fluid coupled analysis is essential to enable an accurate temperature prediction in additive manufacturing. However, numerical simulations of this type are time-consuming, due to the high non-linearity, the underlying large mesh size and the small time step constraints. This paper presents a novel adaptive hyper reduction method for speedin...

The phase field method has been widely adopted in brittle fracture analysis for its ability to handle complex crack topology. This paper presents a novel efficient and robust phase field algorithm for quasi-static brittle fracture analysis. This algorithm overcomes two major issues that affect significantly the numerical cost of the method: the tre...

The paper presents a datadriven framework for parameter identification of welding models. Common identification procedures are based on iterative optimization algorithms which minimize the distance between experimental measures and simulations. The cost of repetitive evaluations of objective functions is prohibitive, especially in welding cases, du...

Standard numerical simulations for optimization or inverse identification of welding processes remain costly and difficult due to their multi-parametric aspect and inherent complexity. The aim of this paper is to propose a non-intrusive strategy for building computational vademecums dedicated to real-time simulations of nonlinear thermo-mechanical...

Simulation based engineering needs usually the construction of computational vademecum in order to take into account the multi-parametric aspect. One example concerns the optimization and inverse identification problems encountered in welding process. This paper presents a non-intrusive a posteriori strategy for constructing quasi-optimal space-tim...

Real time simulations of welding processes remain intractable despite the impressive increasing computing power. This paper presents the case of a thermo-elasto-plastic problem with located moving heat loading. A novel non-intrusive a posteriori reduced order strategy for building multiparametric computational vademecum dedicated to real-time simul...