## Abbreviations  
The following abbreviations are used in this manuscript:  
ABS  
Acrylonitrile Butadiene Styrene  
AI  
Artificial Intelligence  
AUG  
Augmentation  
BNN  
Bayesian Neural Network(s)  
BCNN  
Bayesian Convolution Neural Network(s)  
CAE  
Computer Aided Engineering  
CNN  
Convolutional Neural Network  
CL  
Custom Loss Function  
COL  
Combined Loss Function  
DA  
Data Augmentation  
DTW  
Dynamic Time Warping  
DVL  
DenseVariational Layer  
EMD  
Empirical Mode Decomposition  
EPS  
Equivalent Plastic Strain  
EPSF  
Equivalent Plastic Strain at Failure  
FCL  
Fully Connected Layer  
FCN  
Fully Convolutional Neural Network  
FOC  
Force-Displacement Curve(s)  
FE  
Finite Element  
FFANN  
FeedForward Artificial Neural Network  
GAP  
Global Average Pooling  
GD  
Gradient Descent  
GISSMO  
Generalized Incremental Stress State-Dependent Damage Model  
HL  
Hidden Layer(s)  
HP  
Hyperparameter(s)  
HPO  
Hyperparameter Optimization  
IMF  
Intrinsic Mode Function  
LHS  
Latin Hypercube Sampling  
MCDCNN  
Multi-Channel Deep Convolutional Neural Network  
MCIC  
Material Card Input Curve(s)  
ML  
Machine Learning  
MLP  
Multilayer Perceptron  
MP  
Material Parameter(s)  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**(Hyper-)Parameter** & **Search Range** & \\ \hline Batch Size & 20; 25 *; 30;... ; 150 \\ Number HL & 1 *; 2; 3 \\ Neurons (HL1) & 30; 40; 50 *;... ; 500 \\ Kernel & Normal *; Uniform; Glorot Uniform; Lecun Uniform; Glorot Normal; \\ Initialize (HL) & He Normal; He Uniform \\ Activation (HL1) & Softmax; Softplus; Softsign; Relu *; Sigmoid; Hard Sigmoid \\ Dropout (HL1) & 0.000; 0.025; 0.050 *;... ; 0.250 \\ Neurons (HL2) & 30; 40; 50 *;... ; 500 \\ Kernel & Normal *; Uniform; Glorot Uniform; Lecun Uniform; Glorot Normal; \\ Initialize (HL2) & He Normal; He Uniform \\ Activation (HL2) \begin{table}
\begin{tabular}{l c c c} \hline \hline
**(Hyper-)Parameter** & **Search Range** & \\ \hline Layer 1 Filters & 3; 6; 12; 16; 32; 64; 128 & \\ Layer 1 Kernel Size & 2; 3; 5; 7; 9 & \\ Layer 1 Pooling Size & 2; 3; 5; 7 & \\ Layer 2 Filters & 3; 6; 12; 16; 32; 64; 128 & \\ Layer 2 Kernel Size & 2; 3; 5; 7 & \\ Layer 2 Pooling Size & 2; 3; 5; 7 & \\ Activation Function & Relu; Sigmoid; Tanh & \\ GD Optimizer & Adam; Adamax; Nadam & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: CNN hyperparameter optimization search range. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Run** & **NN** & **Dataset** & **Loss** & **Noise** & **EMD** & **Point** & **Prior** & **Prior** \\  & & & **AUG** & **AUG** & **Number** & **Mean** & **SDV** \\  & & & & & & **SOC** & \\ \hline
1 & Default\_MLP & 1 & MSE & No & No & 200 & - & - \\
2 & Default\_MLP & 2 & MSE & No & No & 200 & - & - \\
3 & Default\_MLP & 3 & MSE & No & No & 200 & - & - \\
4 & Default\_MLP & 4 & MSE & No & No & 200 & - & - \\
5 & Default\_MLP & 1 & COL & No & No & 200 & - & - \\
6 & Default\_MLP & 2 & COL & No & No & 200 & - & - \\
7 & Default\_MLP & 3 & COL & No & No & 200 & - & - \\
8 & Default\_MLP & 4 & COL & No & No & 200 & - & - \\
9 & Default\_MLP & 5 & COL & No & No & 200 & - & - \\
10 & Default\_MLP & 6 & COL & No & No & 200 & - & - \\
11 & Default\_MLP & 7 & COL & No & No & 200 & - & - \\
12 & Default\_MLP & 8 & COL & No & No & 200 & - & - \\
13 & MLPHPO & 3 & COL & No & 200 & - & - \\
14 & Default\_MLP & 3 & COL & Yes & No & 200 & - & - \\
15 & Default\_MLP & 3 & COL & No & Yes & 200 & - & - \\
16 & Default\_CNN & 3 & MSE & No & No & 200 & - & - \\
17 & Default\_CNN & 1 & COL & No & No & 200 & - & - \\
18 & Default\_CNN & 2 & COL & No & No & 200 & - & - \\
19 & Default\_CNN & 3 & COL & No & No & 200 & - & - \\
20 & Default\_CNN & 4 & COL & No & No & 200 & - & - \\
21 & CNN\_[PERSON] & 3 & COL & No & No & 200 & - & - \\
22 & CNN\_[PERSON] & 3 & COL & No & No & 200 & - & - \\
23 & CNN\_[PERSON] & 3 & COL & No & No & 200 & - & - \\
24 & MCDCNN & 3 & COL & No & No & 200 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Settings of the NN-based direct inverse MPI runs.