MPM based simulation for various solid deformation

Solid materials are responsible for many interesting phenomena. There are various types of them such as deformable objects and granular materials. In this paper, we present an MPM based framework to simulate the wide range of solid materials. In this framework, solid mechanics is based on the elastoplastic model, where we use von Mises criterion for deformable objects, and the Drucker-Prager model with non-associated plastic flow rules for granular materials. As a result, we can simulate different kinds of deformation of deformable objects and sloping failure for granular materials

MPM simulation of interacting fluids and solids

The material point method (MPM) has attracted increasing attention from the graphics community, as it combines the strengths of both particle‐ and grid‐based solvers. Like the smoothed particle hydrodynamics (SPH) scheme, MPM uses particles to discretize the simulation domain and represent the fundamental unknowns. This makes it insensitive to geometric and topological changes, and readily parallelizable on a GPU. Like grid‐based solvers, MPM uses a background mesh for calculating spatial derivatives, providing more accurate and more stable results than a purely particle‐based scheme. MPM has been very successful in simulating both fluid flow and solid deformation, but less so in dealing with multiple fluids and solids, where the dynamic fluid‐solid interaction poses a major challenge. To address this shortcoming of MPM, we propose a new set of mathematical and computational schemes which enable efficient and robust fluid‐solid interaction within the MPM framework. These versatile schemes support simulation of both multiphase flow and fully‐coupled solid‐fluid systems. A series of examples is presented to demonstrate their capabilities and performance in the presence of various interacting fluids and solids, including multiphase flow, fluid‐solid interaction, and dissolution

The challenges of modeling and forecasting the spread of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies

Lagrangian Neural Style Transfer for Fluids

Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer approach from images to 3D fluids formulated in a Lagrangian viewpoint. Using particles for style transfer has unique benefits compared to grid-based techniques. Attributes are stored on the particles and hence are trivially transported by the particle motion. This intrinsically ensures temporal consistency of the optimized stylized structure and notably improves the resulting quality. Simultaneously, the expensive, recursive alignment of stylization velocity fields of grid approaches is unnecessary, reducing the computation time to less than an hour and rendering neural flow stylization practical in production settings. Moreover, the Lagrangian representation improves artistic control as it allows for multi-fluid stylization and consistent color transfer from images, and the generality of the method enables stylization of smoke and liquids likewise.Comment: ACM Transaction on Graphics (SIGGRAPH 2020), additional materials: http://www.byungsoo.me/project/lnst/index.htm

Energy-based dissolution simulation using SPH sampling

A novel unified particle-based method is proposed for real-time dissolution simulation that is fast, predictable, independent of sampling resolution, and visually plausible. The dissolution model is derived from collision theory and integrated into a smoothed particle hydrodynamics fluid solver. Dissolution occurs when a solute is submerged in solvent. Physical laws govern the local excitation of solute particles based on kinetic energy: when the local excitation energy exceeds a user-specified threshold (activation energy), the particle will be dislodged from the solid. Solute separation during dissolution is handled using a new Graphics Processing Unit (GPU)-based region growing method. The use of smoothed particle hydrodynamics sampling for both solute and solvent guarantees a predictable and smooth dissolution process and provides user control of the volume change during the phase transition. A mathematical relationship between the activation energy and dissolution time allows for intuitive artistic control over the global dissolution rate. We demonstrate this method using a number of practical examples, including antacid pills dissolving in water, hydraulic erosion of nonhomogeneous terrains, and melting

Integrating Peridynamics with Material Point Method for Elastoplastic Material Modeling

© Springer Nature Switzerland AG 2019. We present a novel integral-based Material Point Method (MPM) using state based peridynamics structure for modeling elastoplastic material and fracture animation. Previous partial derivative based MPM studies face challenges of underlying instability issues of particle distribution and the complexity of modeling discontinuities. To alleviate these problems, we integrate the strain metric in the basic elastic constitutive model by using material point truss structure, which outweighs differential-based methods in both accuracy and stability. To model plasticity, we incorporate our constitutive model with deviatoric flow theory and a simple yield function. It is straightforward to handle the problem of cracking in our hybrid framework. Our method adopts two time integration ways to update crack interface and fracture inner parts, which overcome the unnecessary grid duplication. Our work can create a wide range of material phenomenon including elasticity, plasticity, and fracture. Our framework provides an attractive method for producing elastoplastic materials and fracture with visual realism and high stability

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

Recent progress in deep learning is essentially based on a "big data for small tasks" paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a "small data for big tasks" paradigm, wherein a single artificial intelligence (AI) system is challenged to develop "common sense", enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of "why" and "how", beyond the dominant "what" and "where" framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the "dark matter" of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning. In summary, we argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks.Comment: For high quality figures, please refer to http://wellyzhang.github.io/attach/dark.pd

Part I: Reconstruction of Missing Data in Social Networks Based on Temporal Patterns of Interactions Part II: Constitutive Modeling in Solid Mechanics for Graphics Applications

In Part I, the author presents a mathematical framework based on a self-exciting pointprocess aimed at analyzing temporal patterns in the series of interaction events betweenagents in a social network. We develop a reconstruction model formulated as a constraintoptimization problem that allows one to predict the unknown participants in a portion ofthose events. The results are used to predict the perpetrators of the unsolved crimes in theLos Angeles gang network.Part II discusses the work undertaken by the author in deformable solid body simulation.We first focus on purely elastic solids and develop a method for extending an arbitraryisotropic hyperelastic energy density function to inverted configurations. This energy basedextension is designed to improve robustness of elasticity simulations with extremely largedeformations typical in graphics applications and demonstrates significant improvementsover similar stress based techniques presented in [40, 86]. Moreover, it yields continuousstress and unambiguous stress derivatives in all inverted configurations. We also introducea novel concept of a hyper-elastic model's primary contour which can be used to predict itsrobustness and stability. We demonstrate that our invertible energy-density-based approachoutperforms the popular hyperelastic corotated model [13, 56] and show how to use theprimary contour methodology to improve the robustness of this model to large deformations.We further develop a novel snow simulation method utilizing a user-controllable consti-tutive model defined by an elasto-plastic energy density function integrated with a hybridEulerian/Lagrangian Material Point Method (MPM). The method is continuum based and itshybrid nature allows us to use a regular Cartesian grid to automate treatment of self-collisionand fracture. It also naturally allows us to derive a grid-based implicit integration schemethat has conditioning independent of the number of Lagrangian particles. We demonstratethe power of our method with a variety of snow phenomena