Unlocking the Power of AI/ML in Optimization

Unlocking the Power of AI/ML in Optimization

Merlin Shakila – Director-Software & Data Science

Exploring EinNel’s algorithm development for solving Multi-Disciplinary Design Optimization problem.

Automotive design is a complex process that involves multiple disciplines, such as aerodynamics, structural engineering, materials science, and manufacturing. The optimization of automotive design presents significant challenges due to the complicated relations between these disciplines. However, Artificial Intelligence and Machine Learning (AI/ML) techniques offer a promising solution to tackle these multi-disciplinary design optimization (MDO) problems effectively. In this article, we explore into EinNel’s innovative algorithmic approach, powered by AI/ML, to solve MDO problems in automotive design.


Objective Functions & Feature Derivation from Vehicle Synthesis: The first step in MDO is to define the objectives and constraints of the automotive design problem. EinNel, with its domain knowledge and extensive experience in working with various OEMs, possesses the expertise to define objectives and constraints in automotive design optimization.

EinNel’s deep understanding of the automotive industry, coupled with its scientific software development capabilities, allows for the synthesis of vehicles and the extraction of derived features. These features enhance the optimization process by capturing the relationships between design variables and performance metrics, resulting in more effective and efficient automotive designs.

Optimization Algorithm: EinNel has adopted a diverse range of optimization algorithms to enhance the automotive design process. Some of the techniques explored include RL-based optimization using Proximal Policy Optimization (PPO), Genetic algorithms, and Artificial neural networks combined with optimization algorithms. Each algorithm brings unique advantages and can be applied in different scenarios.

By employing various optimization techniques, EinNel can leverage the strengths of each algorithm and tailor the approach to the specific characteristics of the automotive design problem at hand. EinNel’s expertise in algorithm selection ensures that the most appropriate technique is utilized to achieve optimal results in automotive design optimization.

After applying the optimization algorithm, EinNel was able to identify the Pareto front of all optimal designs. The Pareto front provides decision-makers with a comprehensive view of the optimal tradeoffs available for automotive design. It allows them to explore different design possibilities and select the most suitable solution based on their specific requirements and preferences.

Thus, EinNel empowers automotive designers and engineers to make informed decisions, considering various trade-offs and selecting the design solution that best aligns with their objectives and constraints.