| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Monte Carlo Integration | Example 2 | Error (E)0 | 84 | |
| Variable Selection | Example 1 | TPR100 | 24 | |
| Expectation Estimation | Example 1 | E* Metric0 | 20 | |
| Estimation | Example 2 (rho=0.95) | Estimation Error1.1147 | 16 | |
| Estimation | Example 2 rho=0.75 | Estimation Error1.1597 | 16 | |
| e-good arm identification | Example 2 | False Selection Probability0 | 16 | |
| Numerical solution of singularly perturbed non-linear ODE | Example 3 singularly perturbed non-linear problem with Neumann boundary conditions | L2 Error0 | 12 | |
| Data Assimilation | Example 2 Advection-diffusion-reaction system | Relative Error (ERel,1:T)7.17 | 12 | |
| Classification | Example N=1000 | Classification Risk10 | 12 | |
| Classification | Example N=100 | Classification Risk11 | 12 | |
| Conflict Measurement | Example Synthetic Frame of Discernment with ten elements 4.3 | k[39]5 | 10 | |
| Sparse Modeling | Example 1 (sigma=1) | Pre Error1.1589 | 8 | |
| Sparse Modeling | Example 1 (sigma=0.5) | Pre Error0.2918 | 8 | |
| Estimation Error | Example sigma=1 N=100 1 | Estimation Error0.9063 | 8 | |
| Estimation Error | Example 1 sigma=0.5 (N=100) | Estimation Error0.6252 | 8 | |
| Feasible arm identification | Example 3 | PFS0 | 8 | |
| Feasible arm identification | Example 1 | PFS0.02 | 8 | |
| e-good arm identification | Example 3 | False Selection Probability0.03 | 8 | |
| Solving Non-linear Problem | Example 2 (epsilon = 2^-7) | L2 Error0.0001 | 7 | |
| Solving PDE | Example 6 | L2-error1.27 | 7 | |
| Conflict Measurement | Example Relative Preference Sets 4.4 | K[14] (Left Arrow)0 | 7 | |
| Conflict measurement between Ranked Power Sets | Example Synthetic 8-element frame 4.2 | k[39]0.5 | 5 | |
| Rectified prediction | Example 4 Constitutive law (test) | NMSE4.4 | 4 | |
| Rectified prediction | Example 3 Boundary conditions (test) | NMSE6.6 | 4 | |
| Inference (estimation) | Example Boundary conditions 3 (test) | NMSE (%)0.063 | 4 |