More Like this

1. Microstructure-Sensitive Deformation Modeling and Materials Design with Physics-Informed Neural Networks

   https://doi.org/10.2514/1.J062708  

   Hasan, Mahmudul; Ender_Eger, Zekeriya; Senthilnathan, Arulmurugan; Acar, Pınar (May 2024, AIAA Journal)

   Microstructure-sensitive materials design has become popular among materials engineering researchers in the last decade because it allows the control of material performance through the design of microstructures. In this study, the microstructure is defined by an orientation distribution function. A physics-informed machine learning approach is integrated into microstructure design to improve the accuracy, computational efficiency, and explainability of microstructure-sensitive design. When data generation is costly and numerical models need to follow certain physical laws, machine learning models that are domain-aware perform more efficiently than conventional machine learning models. Therefore, a new paradigm called the physics-informed neural network (PINN) is introduced in the literature. This study applies the PINN to microstructure-sensitive modeling and inverse design to explore the material behavior under deformation processing. In particular, we demonstrate the application of PINN to small-data problems driven by a crystal plasticity model that needs to satisfy the physics-based design constraints of the microstructural orientation space. For the first problem, we predict the microstructural texture evolution of copper during a tensile deformation process as a function of initial texturing and strain rate. The second problem aims to calibrate the crystal plasticity parameters of the Ti-7Al alloy by solving an inverse design problem to match the PINN-predicted final texture prediction and the experimental data. 

   more » « less
2. Ensemble physics informed neural networks: A framework to improve inverse transport modeling in heterogeneous domains

   https://doi.org/10.1063/5.0150016  

   Aliakbari, Maryam; Soltany Sadrabadi, Mohammadreza; Vadasz, Peter; Arzani, Amirhossein (May 2023, Physics of Fluids)

   Modeling fluid flow and transport in heterogeneous systems is often challenged by unknown parameters that vary in space. In inverse modeling, measurement data are used to estimate these parameters. Due to the spatial variability of these unknown parameters in heterogeneous systems (e.g., permeability or diffusivity), the inverse problem is ill-posed and infinite solutions are possible. Physics-informed neural networks (PINN) have become a popular approach for solving inverse problems. However, in inverse problems in heterogeneous systems, PINN can be sensitive to hyperparameters and can produce unrealistic patterns. Motivated by the concept of ensemble learning and variance reduction in machine learning, we propose an ensemble PINN (ePINN) approach where an ensemble of parallel neural networks is used and each sub-network is initialized with a meaningful pattern of the unknown parameter. Subsequently, these parallel networks provide a basis that is fed into a main neural network that is trained using PINN. It is shown that an appropriately selected set of patterns can guide PINN in producing more realistic results that are relevant to the problem of interest. To assess the accuracy of this approach, inverse transport problems involving unknown heat conductivity, porous media permeability, and velocity vector fields were studied. The proposed ePINN approach was shown to increase the accuracy in inverse problems and mitigate the challenges associated with non-uniqueness. 

   more » « less
3. Review of microstructure and micromechanism-based constitutive modeling of polycrystals with a low-symmetry crystal structure

   https://doi.org/10.1557/jmr.2018.333  

   Beyerlein, Irene J.; Knezevic, Marko (November 2018, Journal of Materials Research)

   Predictions of the mechanical response of polycrystalline metals and underlying microstructure evolution and deformation mechanisms are critically important for the manufacturing and design of metallic components, especially those made of new advanced metals that aim to outperform those in use today. In this review article, recent advancements in modeling deformation processing-microstructure evolution and in microstructure–property relationships of polycrystalline metals are covered. While some notable examples will use standard crystal plasticity models, such as self-consistent and Taylor-type models, the emphasis is placed on more advanced full-field models such as crystal plasticity finite elements and Green’s function-based models. These models allow for nonhomogeneity in the mechanical fields leading to greater insight and predictive capability at the mesoscale. Despite the strides made, it still remains a mesoscale modeling challenge to incorporate in the same model the role of influential microstructural features and the dynamics of underlying mechanisms. The article ends with recommendations for improvements in computational speed. 

   more » « less
4. Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes

   https://doi.org/10.1038/s41467-023-39377-6  

   Borate, Prabhav; Rivière, Jacques; Marone, Chris; Mali, Ankur; Kifer, Daniel; Shokouhi, Parisa (June 2023, Nature Communications)

   Abstract Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction. 

   more » « less
5. MicroProcSim: A Software for Simulation of Microstructure Evolution

   https://doi.org/10.1007/s40192-025-00405-6  

   Billah, Md_Maruf; Kilic, Muhammed_Nur_Talha; Hasan, Md_Mahmudul; Eger, Zekeriya_Ender; Mao, Yuwei; Wang, Kewei; Choudhary, Alok; Agrawal, Ankit; Sundararaghavan, Veera; Acar, Pınar (June 2025, Integrating Materials and Manufacturing Innovation)

   Abstract Understanding the large deformation behavior of materials under external forces is crucial for reliable engineering applications. The mechanical properties of materials depend on their underlying microstructures, which change over time during deformation. Experimental observation of these processes is time-consuming and influenced by various conditions. Therefore, we developed , a physics-based simulation tool to replicate the deformation process of cubic microstructures. can predict the evolution of texture, represented by the orientation distribution function (ODF), over time under various loads and strain rates. This software package can be run on both Windows and Linux operating systems. Unlike conventional crystal plasticity finite element software, offers a distinct advantage by rapidly generating deformed textures, as it bypasses incorporating grain morphology. Furthermore, comparisons with existing experimental and computational studies on texture evolution have demonstrated that this software seamlessly replicates real-world material processing conditions through a simple modification of a single input matrix. 

   more » « less