Beyond Human Trial and Error: How AI Algorithms Discover New Solutions
The conventional engineering design approaches are based on human experience, iterative testing, and computational optimization methods to obtain the near-optimal solutions for engineering projects. Engineers are usually constrained by existing concepts and design experience, and so improvements are often obtained through a gradual process of trial and error. This approach has certainly brought a lot of scientific and engineering progress, but it is rather slow, and the search for possible solutions is limited to the imaginations of individuals or pioneers. In contrast, artificial intelligence draws on the knowledge and insight collected across the whole field to produce a variety of design alternatives in a short time. This increases the opportunities to solve complex engineering problems and allows algorithms to find innovative design opportunities that are difficult to obtain with traditional design methods or even impossible at times.
Initially, evolutionary algorithms, neural networks, and generative design systems were iterative techniques requiring human input or computer calculation. After the evolution of computers and programming, automated algorithms subsequently emerged. These automated algorithms are able to iterate, analyze, and optimize large data sets. It would be able to quickly assess thousands of potential design candidates for feasible solutions. Instead of designing one single solution, it can create and evaluate multiple different alternative solutions based on specific engineering objectives or instructions. Such automated computational functions enable engineers to discover out-of-the-box design solutions, which are beyond the reach of human intuition and traditional design thinking.
Despite this, the AI solutions still have to be tested and validated with real-world implementation and testing. Intelligent algorithms can find patterns and optimize the performance of research objects. Meanwhile, engineers remain responsible for making sure that the AI-generated solutions are manufacturable, practical, safe, and adhere to real-world engineering requirements. The future of engineering design is anticipated to be synergy between human creativity and AI. This will change the design process from a trial-and-error iterative process to a more intelligent and efficient process to find engineering solutions.
Learn more about Nextus Innovations Laboratory, Research Support, Project Consultancy
