We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Non-local physics-informed neural networks for forward and inverse solutions of granular flows
AI summary Read the abstract
Despite its title referencing microplastic events in granular flow physics, this paper studies the behavior of dense granular materials — not microplastic pollution. It examines a physics modeling framework using neural networks to predict how granular materials like sand flow and deform, and is not relevant to plastic pollution or human health.
Dense granular flows exhibit nonlocal effects due to stress transmission in microplastic events, especially in quasi-static or slowly sheared regions. Hence, traditional local rheological models fail to capture spatial cooperativity effects that are prominent in many granular systems. The nonlocal granular fluidity (NGF) model addresses this limitation by introducing a diffusive-like partial differential equation for a fluidity field, governed by a key material-dependent parameter: the nonlocal amplitude A. However, determining A from experiments or simulations is known to be difficult and typically requires extensive calibration across multiple geometries. In this work, we present a data-driven platform based on Physics-Informed Neural Networks (PINNs) embedded with the NGF model, capable of solving granular flows in a forward or inverse manner. We show that once trained on transient flow fields, these non-local PINNs can readily infer the material parameters, as well as the pressure and stress fields. These data-driven frameworks allow for accurate recovery of small variations in the nonlocal amplitude, A, which lead to sharp bifurcation-like transitions in the flow field. This approach demonstrates the feasibility of data-driven parameter inference in complex nonlocal models and opens up new possibilities for characterizing granular materials from sparse experimental observations.
More Papers Like This
Non-local physics-informed neural networks for forward and inverse solutions of granular flows
AI summary Read the abstract
Despite its title referencing microplastic events in granular flow physics, this paper studies the behavior of dense granular materials — not microplastic pollution. It examines a physics modeling framework using neural networks to predict how granular materials like sand flow and deform, and is not relevant to plastic pollution or human health.
Spatiotemporal graph neural networks for analyzing the influence mechanisms of river hydrodynamics on microplastic transport processes
AI summary Read the abstract
Spatiotemporal graph neural networks were applied to model how microplastic contamination spreads across connected water bodies over time. This AI-driven modeling approach can improve real-time prediction and management of microplastic pollution in river and lake networks.
Microplastic deposit predictions on sandy beaches by geotechnologies and machine learning models
AI summary Read the abstract
Researchers used geotechnologies and machine learning models to predict microplastic deposition hotspots on sandy beaches, identifying environmental and anthropogenic variables that drive spatial variation in beach microplastic accumulation.
A generative physics-informed machine learning model for soil microplastic accumulation dynamics
AI summary Read the abstract
Researchers developed a physics-informed machine learning model to simulate and predict microplastic accumulation dynamics in soils, combining experimental data with mechanistic equations to overcome the limitations of heterogeneous field conditions. The integrated model outperformed purely data-driven approaches in predicting MP transport and retention in soil.
Mapping the plastic legacy: Geospatial predictions of a microplastic inventory in a complex estuarine system using machine learning
AI summary Read the abstract
Researchers applied machine learning techniques to develop geospatial predictions of microplastic inventory in a complex estuarine system, overcoming the limitations of coarse ocean basin models by accounting for the intricate geomorphological and hydrodynamic conditions that govern sediment-associated microplastic distribution.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.