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Yes / Physics-based skin deformation methods can greatly improve the realism of character animation, but require non-trivial training, intensive manual intervention, and heavy numerical calculations. Due to these limitations, it is generally time-consuming to implement them, and difficult to achieve a high runtime efficiency. In order to tackle the above limitations caused by numerical calculations of physics-based skin deformation, we propose a simple and efficient analytical approach for physics-based skin deformations. Specifically, we (1) employ Fourier series to convert 3D mesh models into continuous parametric representations through a conversion algorithm, which largely reduces data size and computing time but still keeps high realism, (2) introduce a partial differential equation (PDE)-based skin deformation model and successfully obtain the first analytical solution to physics-based skin deformations which overcomes the limitations of numerical calculations. Our approach is easy to use, highly efficient, and capable to create physically realistic skin deformations. / This research is supported by the PDE-GIR project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement (No.778035), the National Natural Science Foundation of China (Grant No.51475394), and Innovate UK (Knowledge Transfer Partnerships KTP.010860). Shaojun Bian is also supported by Chinese Scholar Council. Xiaogang Jin is supported by the Key Research and Development Program of Zhejiang Province (No.2018C01090) and the National Natural Science Foundation of China (No.61732015).

Changes in climatic regimes and land use have led to increases in wildfire activities around the world. Wildfires are now happening more frequently, at higher altitudes, and higher severities. Adverse impacts of wildfires can last years after the fire has been contained through post-fire geohazards, such as shallow landslides. Post-wildfire shallow landslides are often mobilized by rainfall and due to fire-induced changes in soil and land cover properties and near-surface processes. This study aims to develop a physics-based framework to evaluate the stability of burned hillslopes against rainfall-triggered shallow landslides. A coupled hydromechanical infiltration model is developed by employing a closed-form solution of the Richards equation. The model is integrated into an infinite slope stability analysis to capture the effect of temporal changes in the pressure head profile of an unsaturated vegetated slope on its stability. The proposed model considers the antecedent condition of soil and vegetation cover, the time-varying nature of rainfall intensity, and wildfire-induced changes in soil properties, root reinforcement, transpiration rate, and canopy interception. The efficacy of the proposed framework is illustrated through modeling a case study in the Las Lomas watershed in California, USA. The watershed was a part of a larger area that was burned in the San Gabriel Complex Fire (consisting of two separate fires, the Fish Fire and the Reservoir Fire) in 2016. Three years later, during a heavy rainstorm in January 2019, the affected area, including the Las Lomas watershed, experienced widespread landslides. The proposed framework is then integrated into a geographic information system (GIS) to generate a susceptibility map of post-wildfire rainfall-triggered shallow landslides. The applicability of the proposed framework at a regional scale is tested for the entire area affected by the San Gabriel Complex Fire to model the observed shallow landslides within the boundaries of the Fish Fire and the Reservoir Fire. The findings of this study can be used to warn the community of post-wildfire shallow landslides activities.

The spatial variation of wave climate plays a crucial role in erosion, sediment transport, and the design of management actions in coastal areas. Low energy wave systems occur frequently and over a wide range of geographical areas. There is a lack of studies assessing wave model performance in low-energy environments at a regional scale. Therefore, this research aims to model a low energy wave system using a high-resolution dataset. The specific objectives of this study involves 1) using cluster analysis and extensive field measurements to understand the spatial behavior of ocean waves, 2) develop a physics based model of wind-driven waves using high-resolution measurements, and 3) compare machine learning and physics-based models in simulating wave climates. The findings of this study indicate that clustering can effectively assess the spatial variation of the wave climate in a low energy system, with depth identified as the most important influencing factor. Additionally, the physics-based model showed varying performance across different locations within the study area, accurately simulating wave climates in some locations but not in others. Finally, the machine learning model demonstrated overall acceptable performance and accuracy in simulating wave climates and revealed better agreement with observed data in estimating central tendency compared to the physics-based model. The physics-based model performed more favorably for dispersion metrics. These findings contribute to our understanding of coastal dynamics. By providing insights into the spatial behavior of wave climates in low energy systems and comparing the performance of physics-based model and machine learning model, this research contributes to the development of effective coastal management strategies and enhances our understanding of coastal processes.

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The genetic algorithm (GA) is a popular random search and optimization method inspired by the concepts of crossover, random mutation, and natural selection from evolutionary biology. The real-valued genetic algorithm (RGA) is an improved version of the genetic algorithm designed for direct operation on real-valued variables. In this work, a modified version of a genetic algorithm is introduced, which is called a modified genetic algorithm with micro-movement (MGAM). It implements a particle swarm optimization(PSO)-inspired micro-movement phase that helps to improve the convergence rate, while employing the e'cient GA mechanism for maintaining population diversity. In order to test the capability of the MGAM, we firrst implement it on five generally used test functions. Then we test the MGAM on two typical nonlinear dynamical systems. The performance of the MGAM is compared to a basic RGA on all these applications. Finally, we implement the MGAM on the most important application, which is the plasma physics-based model of the solar wind-driven magnetosphere-ionosphere system (WINDMI). In order to use this model for real-time prediction of geomagnetic activity, the model parameters require up-dating every 6-8 hours. We use the MGAM to train the parameters of the model in order to achieve the lowest mean square error (MSE) against the measured auroral electrojet (AL) and Dst indices. The performance of the MGAM is compared to the RGA on historical geomagnetic storm datasets. While the MGAM performs substantially better than the RGA when evaluating standard test functions, the improvement is about 6-12 percent when used on the 20D nonlinear dynamical WINDMI model.