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Found 12 papers, 2 papers with code
Pattern formation in dense populations studied by inference of nonlinear diffusion-reaction mechanisms
The biological and chemical elements that form the basis of this process, like cells and proteins, occupy finite mass and volume and interact during migration.
FP-IRL: Fokker-Planck-based Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes
We identify specific manifestations of this isomorphism and use them to create a novel physics-aware IRL algorithm, FP-IRL, which can simultaneously infer the transition and reward functions using only observed trajectories.
Partial differential equation-based inference of migration and proliferation mechanisms in cancer cell populations
Targeting signaling pathways that drive cancer cell migration or proliferation is a common therapeutic approach.
Machine Learning in Heterogeneous Porous Materials
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research.
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference
In this work we employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities.
Li$_x$CoO$_2$ phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories
Li$_xTM$O$_2$ (TM={Ni, Co, Mn}) are promising cathodes for Li-ion batteries, whose electrochemical cycling performance is strongly governed by crystal structure and phase stability as a function of Li content at the atomistic scale.
Biomembranes undergo complex, non-axisymmetric deformations governed by Kirchhoff-Love kinematics and revealed by a three dimensional computational framework
Lipid bilayers are represented as spline-based surfaces immersed in a 3D space; this enables modeling of a wide spectrum of membrane geometries, boundary conditions, and deformations that are physically admissible in a 3D space.
Bayesian neural networks for weak solution of PDEs with uncertainty quantification
As both Dirichlet and Neumann BCs are specified as inputs to NNs, a single NN can solve for similar physics, but with different BCs and on a number of problem domains.
System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities.
Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys
Specifically, we have developed an integrable deep neural network (IDNN) that can be trained to free energy derivative data obtained from atomic scale models and statistical mechanics, then analytically integrated to recover a free energy density function.
Second Order Threshold Dynamics Schemes for Two Phase Motion by Mean Curvature
As a first, rigorous step in addressing this shortcoming, we present two different second order accurate versions of two-phase threshold dynamics.
Numerical Analysis Numerical Analysis 65M06, 65M12
On the Voronoi Implicit Interface Method
We present careful numerical convergence studies, using parameterized curves to reach very high resolutions in two dimensions, of a level set method for multiphase curvature motion known as the Voronoi implicit interface method.
Numerical Analysis 65M06
