Mass Movement Processes and Landforms

Dramis, F.

doi:10.1002/9781118786352.wbieg0929.pub2

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…• Spring force We denote the spring constant as k and balance length as L. The pairwise force from v i to v j is k(r ij − L)n ij and its potential energy is 0.5k(r ij − L) 2 , where r ij = r j − r i is the Euclidean distance and n ij = rj −ri rj −ri is the unit vector pointing from v i to v j . We set k = 2.0 and L = 1.0 in our simulations.…”

Section: Details About Simulation and Experimentsmentioning

confidence: 99%

“…Interacting particle systems play a key role in nature and engineering as they govern planetary motion [1], mass movement processes [2] such as landslides and debris flow, bulk material packaging [3], magnetic particle transport for biomaterials [4], and many more. Since the macroscopic behavior of such particle systems is the result of interactions between individual particles, knowing the governing interaction law is required to better understand, model and predict the kinetic behaviour of these systems.…”

Section: Introductionmentioning

confidence: 99%

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Learning Physics-Consistent Particle Interactions

Han¹,

Kammer²,

Fink³

2022

Preprint

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Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hidden in many cases. Machine learning methods have the potential to learn the behavior of interacting particle systems by combining experiments with data analysis methods. However, most existing algorithms focus on learning the kinetics at the particle level. Learning pairwise interaction, e.g., pairwise force or pairwise potential energy, remains an open challenge. Here, we propose an algorithm that adapts the Graph Networks framework, which contains an edge part to learn the pairwise interaction and a node part to model the dynamics at particle level. Different from existing approaches that use neural networks in both parts, we design a deterministic operator in the node part. The designed physics operator on the nodes restricts the output space of the edge neural network to be exactly the pairwise interaction. We test the proposed methodology on multiple datasets and demonstrate that it achieves considerably better performance in inferring correctly the pairwise interactions while also being consistent with the underlying physics on all the datasets than existing purely data-driven models. The developed methodology can support a better understanding and discovery of the underlying particle interaction laws, and hence guide the design of materials with targeted properties.Recent efforts on developing machine learning (ML) methods for the discovery of particle interaction laws have shown great potential in overcoming these challenges [5][6][7][8][9][10][11]. These ML methods, such as the Graph Network-based Simulators (GNS) [12] for simulating physical processes, Dynamics Extraction From cryo-em Map (DEFMap) [13] for learning the atomic fluctuation in proteins, the SchNet [14,15] which can learn the molecular energy and the neural relational inference model (NRI) [16] developed for inferring heterogeneous interactions, can be applied on various types of interacting particle systems such as water particles, sand and plastically deformable particles. They allow implicit and explicit learning of the mechanical behavior of particle systems without prior Preprint. Under review.

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Section: Details About Simulation and Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Physics-Consistent Particle Interactions

Han¹,

Kammer²,

Fink³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Interacting particle systems play a key role in nature and engineering as they govern planetary motion ( 1 ), mass movement processes ( 2 ) such as landslides and debris flow, bulk material packaging ( 3 ), magnetic particle transport for biomaterials ( 4 ), and many more. Since the macroscopic behavior of such particle systems is the result of interactions between individual particles, knowing the governing interaction law is required to better understand, model, and predict the kinetic behavior of these systems.…”

Section: Introductionmentioning

confidence: 99%

Learning physics-consistent particle interactions

2022

View full text Add to dashboard Cite

Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hidden in many cases. Machine learning methods have the potential to learn the behavior of interacting particle systems by combining experiments with data analysis methods. However, most existing algorithms focus on learning the kinetics at the particle level. Learning pairwise interaction, e.g., pairwise force or pairwise potential energy, remains an open challenge. Here, we propose an algorithm that adapts the Graph Networks framework, which contains an edge part to learn the pairwise interaction and a node part to model the dynamics at particle level. Different from existing approaches that use neural networks in both parts, we design a deterministic operator in the node part that allows to precisely infer the pairwise interactions that are consistent with underlying physical laws by only being trained to predict the particle acceleration. We test the proposed methodology on multiple datasets and demonstrate that it achieves superior performance in inferring correctly the pairwise interactions while also being consistent with the underlying physics on all the datasets. While the previously proposed approaches are able to be applied as simulators, they fail to infer physically consistent particle interactions that satisfy Newton’s laws. Moreover, the proposed PIG’N’PI also outperforms the other baseline models in terms of generalization ability to larger systems and robustness to significant levels of noise. The developed methodology can support a better understanding and discovery of the underlying particle interaction laws, and hence guide the design of materials with targeted properties.

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Mass Movement Processes and Landforms

Cited by 2 publications

References 11 publications

Learning Physics-Consistent Particle Interactions

Learning Physics-Consistent Particle Interactions

Learning physics-consistent particle interactions

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