screening predicting and computer experiments

Here is an academic-style English article on the topic of *Screening, Predicting, and Computer Experiments*:

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# Screening, Predicting, and Computer Experiments: Advances in Computational Modeling

## Abstract
Computer experiments have become indispensable tools for scientific discovery and industrial optimization. This paper reviews key methodologies in screening influential factors, constructing predictive models, and designing efficient computer experiments. We discuss Gaussian process emulation, sensitivity analysis techniques, and sequential design strategies that bridge simulations with real-world applications.

## 1. Introduction
With the exponential growth of computational power, large-scale simulations now complement—and sometimes replace—physical experiments in fields ranging from aerospace engineering to drug discovery. However, complex computational models often require sophisticated statistical approaches to:
1) *Screen* critical input variables from high-dimensional parameter spaces;
2) *Predict* system behavior using limited simulation runs;
3) *Optimize* experimental designs for computationally expensive models.

## 2. Screening Techniques
2.1 Sensitivity Analysis
Global sensitivity methods like Sobol indices [1] quantify how input variability affects output uncertainty. Elementary effects (Morris method) provide computationally efficient screening for models with >100 parameters [2].

2.2 Variable Selection
Regularized regression (LASSO) and tree-based algorithms (Random Forest) automatically identify non-influential factors in high-dimensional problems [3].

## 3. Predictive Modeling via Emulation
Gaussian Process (GP) regression has emerged as the gold standard for surrogate modeling:

$$f(\mathbf{x}) \sim \mathcal{GP}\big(m(\mathbf{x}), k(\mathbf{x}, \mathbf{x}')\big)$$

where $k(\cdot,\cdot)$ governs spatial correlation between design points [4]. Recent advances include:
- Deep kernel learning for non-stationary processes
- Multi-fidelity modeling combining cheap/low-accuracy and expensive/high-fidelity simulations

## 4. Experimental Design Strategies
4.1 Space-Filling Designs
Latin Hypercube Sampling (LHS) and Sobol sequences ensure uniform coverage of input space [5].

4.2 Adaptive Sampling
Expected Improvement (EI) criteria balance exploration vs exploitation in sequential designs [6]:

$$\text{EI}(\mathbf{x}) = \mathbb{E}\big[\max(f_{\min} - Y(\mathbf{x}), 0)\big]$$

## 5. Case Studies
Aerodynamics: GP emulators reduced CFD simulation costs by 92