arXiv subjects

Yongming Liu

Publications and source records attributed to Yongming Liu.

25 records · Page 2Linked to original sources

arXiv2021

Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks

To evaluate the robustness gain of Bayesian neural networks on image classification tasks, we perform input perturbations, and adversarial attacks to the state-of-the-art Bayesian neural networks, with a benchmark CNN model as reference. The attacks are selected to simulate signal interference and cyberattacks towards CNN-based machine learning systems. The result shows that a Bayesian neural network achieves significantly higher robustness against adversarial attacks generated against a deterministic neural network model, without adversarial training. The Bayesian posterior can act as the safety precursor of ongoing malicious activities. Furthermore, we show that the stochastic classifier after the deterministic CNN extractor has sufficient robustness enhancement rather than a stochastic feature extractor before the stochastic classifier. This advises on utilizing stochastic layers in building decision-making pipelines within a safety-critical domain.

cs.LG↗

arXiv2021

Energy-based time derivative damage accumulation model under uniaxial and multiaxial random loadings

A new fatigue life prediction method using the energy-based approach under uniaxial and multiaxial random loadings is proposed in this paper. One unique characteristic of the proposed method is that it uses time-derivative damage accumulation model compared to the classical cycle-based damage accumulation model. Thus, damage under arbitrary random loading can be directly obtained using time-domain integration without cycle counting (e.g., rain-flow cycle counting method in classical fatigue analysis). First, a brief review of existing models is given focusing on their applicability to uniaxial/multiaxial, constant/random, and high cycle fatigue/low cycle fatigue loading regimes. It is observed that most existing models are only applicable to certain loading conditions and many of them are not applicable/validated under random loadings. Next, a time-derivative damage accumulation model under uniaxial random loading is proposed. The proposed damage function is inspired by a time-domain fatigue crack growth model. The fatigue life is obtained by integrating the damage function following random energy loading histories. Following this, an equivalent energy concept for general multiaxial loading conditions is used to convert the random multiaxial loading to an equivalent random uniaxial loading, where the time-derivative damage model can be used. Finally, the proposed model is validated with extensive experimental data from open literature and in-house testing data under various constant and random spectrum loadings. Conclusions and future work are suggested based on the findings from this study.

physics.app-ph↗

arXiv2021

Robust Data-Driven Discovery of Partial Differential Equations under Uncertainties

Robust physics (e.g., governing equations and laws) discovery is of great interest for many engineering fields and explainable machine learning. A critical challenge compared with general training is that the term and format of governing equations is not known as a prior. In addition, significant measurement noise and complex algorithm hyperparameter tuning usually reduces the robustness of existing methods. A robust data-driven method is proposed in this study for identifying the governing Partial Differential Equations (PDEs) of a given system from noisy data. The proposed method is based on the concept of Progressive Sparse Identification of PDEs (PSI-PDE or $ψ$-PDE). Special focus is on the handling of data with huge uncertainties (e.g., 50$\%$ noise level). Neural Network modeling and fast Fourier transform (FFT) are implemented to reduce the influence of noise in sparse regression. Following this, candidate terms from the prescribed library are progressively selected and added to the learned PDEs, which automatically promotes parsimony with respect to the number of terms in PDEs as well as their complexity. Next, the significance of each learned terms is further evaluated and the coefficients of PDE terms are optimized by minimizing the L2 residuals. Results of numerical case studies indicate that the governing PDEs of many canonical dynamical systems can be correctly identified using the proposed $ψ$-PDE method with highly noisy data. One great benefit of proposed algorithm is that it avoids complex algorithm modification and hyperparameter tuning in most existing methods. Limitations of the proposed method and major findings are presented.

math.NA↗

arXiv2020

FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction

An innovative physics-guided learning algorithm for predicting the mechanical response of materials and structures is proposed in this paper. The key concept of the proposed study is based on the fact that physics models are governed by Partial Differential Equation (PDE), and its loading/ response mapping can be solved using Finite Element Analysis (FEA). Based on this, a special type of deep convolutional neural network (DCNN) is proposed that takes advantage of our prior knowledge in physics to build data-driven models whose architectures are of physics meaning. This type of network is named as FEA-Net and is used to solve the mechanical response under external loading. Thus, the identification of a mechanical system parameters and the computation of its responses are treated as the learning and inference of FEA-Net, respectively. Case studies on multi-physics (e.g., coupled mechanical-thermal analysis) and multi-phase problems (e.g., composite materials with random micro-structures) are used to demonstrate and verify the theoretical and computational advantages of the proposed method.

cs.CE↗

arXiv2020

Exfoliation of Two-Dimensional Nanosheets of Metal Diborides

The metal diborides are a class of ceramic materials with crystal structures consisting of hexagonal sheets of boron atoms alternating with planes of metal atoms held together with mixed character ionic/covalent bonds. Many of the metal diborides are ultrahigh temperature ceramics like HfB$_2$, TaB$_2$, and ZrB$_2$, which have melting points above 3000$^\circ$C, high mechanical hardness and strength at high temperatures, and high chemical resistance, while MgB$_2$ is a superconductor with a transition temperature of 39 K. Here we demonstrate that this diverse family of non-van der Waals materials can be processed into stable dispersions of two-dimensional (2D) nanosheets using ultrasonication-assisted exfoliation. We generate 2D nanosheets of the metal diborides AlB$_2$, CrB$_2$, HfB$_2$, MgB$_2$, NbB$_2$, TaB$_2$, TiB$_2$, and ZrB$_2$, and use electron and scanning probe microscopies to characterize their structures, morphologies, and compositions. The exfoliated layers span up to micrometers in lateral dimension and reach thicknesses down to 2-3 nm, while retaining their hexagonal atomic structure and chemical composition. We exploit the convenient solution-phase dispersions of exfoliated CrB$_2$ nanosheets to incorporate them directly into polymer composites. In contrast to the hard and brittle bulk CrB$_2$, we find that CrB$_2$ nanocomposites remain very flexible and simultaneously provide increases in the elastic modulus and the ultimate tensile strength of the polymer. The successful liquid-phase production of 2D metal diborides enables their processing using scalable low-temperature solution-phase methods, extending their use to previously unexplored applications, and reveals a new family of non-van der Waals materials that can be efficiently exfoliated into 2D forms.

cond-mat.mtrl-sci↗

arXiv2018

ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data

Imbalanced data widely exists in many high-impact applications. An example is in air traffic control, where we aim to identify the leading indicators for each type of accident cause from historical records. Among all three types of accident causes, historical records with 'personnel issues' are much more than the other two types ('aircraft issues' and 'environmental issues') combined. Thus, the resulting dataset is highly imbalanced, and can be naturally modeled as a network. Up until now, most existing work on imbalanced data analysis focused on the classification setting, and very little is devoted to learning the node representation from imbalanced networks. To address this problem, in this paper, we propose Vertex-Diminished Random Walk (VDRW) for imbalanced network analysis. The key idea is to encourage the random particle to walk within the same class by adjusting the transition probabilities each step. It resembles the existing Vertex Reinforced Random Walk in terms of the dynamic nature of the transition probabilities, as well as some convergence properties. However, it is more suitable for analyzing imbalanced networks as it leads to more separable node representations in the embedding space. Then, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, in which context sampling uses VDRW and the label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs in different classes. Experimental results demonstrate that ImVerde based on VDRW outperforms state-of-the-art algorithms for learning network representation from imbalanced data.

cs.SI↗

arXiv2017

Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design

Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable ICME, it is required that (1) a structural feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. The majority of existing structural presentation schemes rely on the designer's understanding of specific material systems to identify geometric and statistical features, which could be biased and insufficient for reconstructing physically meaningful microstructures of complex material systems. In this paper, we develop a feature learning mechanism based on convolutional deep belief network to automate a two-way conversion between microstructures and their lower-dimensional feature representations, and to achieves a 1000-fold dimension reduction from the microstructure space. The proposed model is applied to a wide spectrum of heterogeneous material systems with distinct microstructural features including Ti-6Al-4V alloy, Pb63-Sn37 alloy, Fontainebleau sandstone, and Spherical colloids, to produce material reconstructions that are close to the original samples with respect to 2-point correlation functions and mean critical fracture strength. This capability is not achieved by existing synthesis methods that rely on the Markovian assumption of material microstructures.

cond-mat.mtrl-sci↗