Article
Engineering, Civil
Huimin Lu, Yadong Teng, Yujie Li
Summary: In recent years, the methods of loading and transporting rigid objects have improved. However, controlling the shape of deformable objects during transportation has attracted attention. This study uses contrastive learning to solve the shape control problem of deformable objects and improves the model's representation ability by constructing an encoder to extract effective information. Experimental results show significant performance improvements compared to baseline methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Mechanical
Christopher C. Sullivan, Hiroki Yamashita, Hiroyuki Sugiyama
Summary: This study explores the model order reduction of high-fidelity off-road mobility models to address computational intensity. A model order reduction procedure for the tire-soil interaction model is developed using proper orthogonal decomposition (POD) and integrated into the off-road mobility simulation framework with high-performance computing. A method of mode adaptation through interpolation on a tangent space of the Grassmann manifold is investigated to overcome the limitation of POD modes being accurate only for specific scenarios. Numerical examples demonstrate the effectiveness of POD modes in reducing computational time while retaining predictive accuracy.
JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS
(2022)
Article
Energy & Fuels
Andreas Koenig-Haagen, Moritz Faden, Gonzalo Diarce
Summary: Macro-encapsulation of phase change material is a promising solution for enhancing the thermal power of latent heat thermal energy storage systems. However, simulating the charging process using detailed CFD models is computationally expensive. This study presents a new approach to create a reduced order model using look-up tables, which significantly increases the accuracy of the system simulation.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Chemistry, Physical
Maria Carta, Francesco Delogu, Andrea Porcheddu
Summary: Mechanochemistry offers new and greener synthetic routes by enabling solvent-free chemical reactions, and understanding the relationships between processing variables, powders' mechanical behavior, and chemical reactivity is crucial for its practical exploitation. A phenomenological kinetic model proposed in this work can help experimentalists decipher the mechanical, chemical, and statistical factors underlying mechanochemical reactions, providing insights into the underlying mechanochemistry through satisfactorily fitting experimental datasets with the model equations.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2021)
Article
Materials Science, Multidisciplinary
Jialu Song, Hujin Xie, Yongmin Zhong, Jiankun Li, Chengfan Gu, Kup-Sze Choi
Summary: The paper introduces a new reduced-order nonlinear Kalman filter to emulate nonlinear behaviors of biological deformable tissues for accurate simulation of tissue physical deformation in real time. The approach reduces the order of the nonlinear state-space equation to decrease computational cost, constructing an extended Kalman filter to calculate tissue physical deformation behaviors online. Simulation results and comparison analysis verify the effectiveness of the proposed method.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
(2022)
Article
Computer Science, Software Engineering
Min Hyung Kee, Kiwon Um, Wooseok Jeong, Junghyun Han
Summary: This paper introduces a novel integration method that conserves energy and momentum, resolving the issue of uncontrolled dissipation while maintaining real-time performance and simulation stability. Additionally, users can directly control energy and momentum to create desired deformable and global motions, making it ideal for real-time applications like virtual/augmented reality and games.
ACM TRANSACTIONS ON GRAPHICS
(2021)
Article
Physics, Multidisciplinary
M. Lisa Manning
Summary: The field of soft matter physics has expanded rapidly in recent decades, revealing the importance of entropy, elasticity, and geometry in understanding various materials and systems. Similarly, the fields of biological physics and the physics of living systems have gained recognition as independent areas of study, aided by tools from molecular and cell biology and optical physics. This Essay explores two future challenges at the intersection of these two fields: the characterization of emergent behavior and the manipulation of highly deformable active objects. Progress in these areas holds the potential for creating adaptive smart materials and advancing our understanding of biological function, particularly in the fight against disease.
PHYSICAL REVIEW LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Ashis Pati, Alexander Lerch
Summary: This paper introduces a novel method to structure the latent space of a variational auto-encoder to explicitly encode different continuous-valued attributes. The proposed approach leads to disentangled and interpretable latent spaces, enabling effective manipulation of a wide range of data attributes.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Robotics
P. Mitrano, D. McConachie, D. Berenson
Summary: The world outside laboratories often does not adhere to the assumptions of our models, especially for complex high-degree of freedom systems. This article discusses deploying robots in unstructured environments and proposes methods to address unreliable model states.
Article
Engineering, Mechanical
Ali Raoofian, Albert Peiret, Jozsef Kovecses, Marek Teichmann
Summary: Simulation of complex systems requires the division to subsystems and coupling them through co-simulation. This study proposes an efficient interface model for non-smooth mechanical systems to improve simulation accuracy, functionality, and stability.
MECHANISM AND MACHINE THEORY
(2022)
Article
Robotics
Suyoung Choi, Gwanghyeon Ji, Jeongsoo Park, Hyeongjun Kim, Juhyeok Mun, Jeong Hyun Lee, Jemin Hwangbo
Summary: Simulation-based reinforcement learning approaches are at the forefront of legged robot control innovation. However, they are still unable to perform well on soft and deformable terrains, especially at high speed. To address this issue, we propose a versatile and computationally efficient granular media model for reinforcement learning, coupled with an adaptive control architecture that can identify terrain properties and enhance the locomotion performance of the legged robot.
Article
Computer Science, Software Engineering
Jerry Hsu, Nghia Truong, Cem Yuksel, Kui Wu
Summary: Initializing simulations of deformable objects is crucial, and to tackle the sagging problem, the authors propose a novel solution that avoids solving a global nonlinear optimization problem by performing the initialization in two stages. This solution can be applied to various simulation systems and materials and can handle frictional contact much faster than prior work.
ACM TRANSACTIONS ON GRAPHICS
(2022)
Review
Robotics
Hang Yin, Anastasia Varava, Danica Kragic
Summary: The importance of perceiving and handling deformable objects in our daily life is highlighted in this paper, with a focus on automating tasks like food handling and garment sorting. Advances in data-driven approaches, combined with traditional control and planning methods, offer viable solutions to these challenges. A learning perspective is utilized to unify discussions on analytical and data-driven methods, addressing the integration of model priors and task data in manipulating deformable objects.
Article
Robotics
Bokui Shen, Zhenyu Jiang, Christopher Choy, Silvio Savarese, Leonidas J. J. Guibas, Anima Anandkumar, Yuke Zhu
Summary: This article introduces ACID, an action-conditional visual dynamics model for volumetric deformable objects. ACID integrates implicit representations and geodesics-based contrastive learning techniques to accurately represent deformable dynamics and identify state changes under non-rigid deformations. The evaluation shows that ACID outperforms existing approaches in geometry, correspondence, and dynamics predictions. The application of the ACID model in goal-conditioned deformable manipulation tasks leads to a 30% increase in task success rate over the strongest baseline. Furthermore, the simulation-trained ACID model is successfully applied to real-world objects for manipulation tasks.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Hai Wang, Xiaoyu Xiang, Yapeng Tian, Wenming Yang, Qingmin Liao
Summary: This research proposes a deformable attention network called STDAN for increasing the spatial-temporal resolution of low-resolution and low frame rate videos. It includes a LSTFI module for excavating content from neighboring input frames and a STDFA module for capturing and aggregating spatial and temporal contexts in dynamic video frames. Experimental results show that this approach outperforms existing STVSR methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)