## Main Research Topic: Uncertainty Quantification (UQ)

## Why do we need to consider UNCERTAINTIES ?

## Topic 1: Deterministic Input-Output Model Construction

(Data-Driven & Physics-Based)

## Topic 2-1: Forward Uncertainty Quantification

(Forward UQ; Forward Uncertainty Propagation)

## Topic 2-2: Inverse Uncertainty Quantification

(Inverse UQ; Inverse Uncertainty Propagation)

## Topic 2-3: Model Uncertainty Quantification

(Model UQ)

**#1. Finite Element (FE) Model Updating using Deep Reinforcement Learning Agent**

- Finite element models may have errors, due to various reasons (Sehgal and Kumar 2016):

- Wrong inputs of material properties (e.g., mass density, Young's modulus, etc.)
- Geometric simplification in modeling complex shapes
- Non-consideration of non-linear properties
- Incorrect modeling of boundary conditions and joints
- Poor quality mesh design
- Computational approximation (e.g., rounding off)

- These factors need to be carefully corrected for diminishing the model errors. Here, this correction process is an optimization problem with multiple design variables, which may generate expensive computational cost.
- I'm interested in solving this expensive optimization problem using an
**OPTIMIZATION ROBOT**,**well-trained using the deep reinforcement learning algorithm**.

## #2. Finite Element Reliability Analysis

- It will be updated soon.

## #3. Structural Condition Monitoring by Introducing Bayesian Inference

- -

## #4. Probabilistic Finite Element Analysis

- -