Package Overview
mlmod methods available for interfacing the machine learning approaches with simulations.Name |
Tag / Mode |
Role |
Description |
MPI |
|---|---|---|---|---|
General ML Force-Laws |
F_ML1 |
force |
General force law for groups of particles \(X=\{X_i\}_{i\in\mathcal{G}}\) based on a specified machine learning model, \(F(X,V,F,I_T,t)\). |
|
General ML Dynamics |
Dyn_ML1 |
dynamics |
General dynamics based on specified machine learning model, \([X^{n+1},V^{n+1}] = \Gamma[X^n,V^n]\). |
|
Quantity-of-Interest ML Methods |
QoI_ML1 |
quantity of interest |
For computing a Quantity-of-Interest (QoI) as defined by a specified machine learning model, \(A(X,V,F,I_T,t)\). Available for use in subsequent calculations within simulations. |
|
General Mobility ML Model |
dX_MF_ML1 |
dynamics |
Mobility based on specified machine learning model, \({dX}/{dt} = M(X)F\). |
|
Pairwise Mobility ML Model |
dX_MF_Q1_ML1_Pair |
dynamics |
Mobility with pairwise-structure and specified machine learning model, \({dX}/{dt} = M(X)F + F_{thm}\). |
|
Collective N-to-N Mobility ML Model |
dX_MF_Q1_ML1_N2N |
dynamics |
Mobility with general structure and specified machine learning model, \({dX}/{dt} = M(X)F + F_{thm}\). |
|
Mobility Test |
dX_MF |
dynamics |
For testing mobility calculations, \({dX}/{dt} = M(X)F\). |
|
Particle ML Force-Laws |
F_X_ML1 |
force |
Force law for individual particles \(X_i\) based on specified machine learning model, \(F_i(X_i,V_i,F_i,{I_T}_i,t)\). |
yes |
Pairwise ML Force-Law |
F_Pair_ML1 |
force |
Force law for pairs of particles based on specified machine learning model, \(F_{ij}(X_{ij},V_{ij},\) \(F_{ij},{I_T}_{ij},t)\). |
User Guide
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