Creative Engineering vs Inventive Engineering

Creative engineering involves the use of imagination, design, and innovation to create new and useful solutions to problems. It’s all about thinking outside the box and finding unique ways to solve complex challenges.

Inventive engineering involves the use of creativity and intelligence to invent new products, processes, or systems that improve people’s lives or solve real-world problems in a way that has never been done before.

To be easier for you to make the difference between creative engineering and inventive engineering, please find below some illustrative examples. In the case of creative engineering we can exemplify:

  • The design of the Tesla Model S electric car, which involved developing new battery technology and using lightweight materials to optimize performance and efficiency.
  • The creation of the Burj Khalifa, the world’s tallest building, which required innovative engineering techniques to support the weight of the structure and withstand high winds and seismic activity.
  • The development of the Segway personal transporter, which involved combining sensors, motors, and control systems in a novel way to enable smooth and intuitive movement.
  • The creation of the Mars Rover, which required engineers to design a highly capable and reliable robotic vehicle capable of navigating complex terrain and conducting scientific experiments.
  • The design of the Airbus A380 superjumbo jet, which required advanced engineering techniques to manage the weight and complexity of the aircraft while providing a comfortable and efficient passenger experience.

In the case of inventive engineering below we introduce some examples:

  • The development of the first 3D printer by Chuck Hull in 1983, which revolutionized the manufacturing industry by allowing for the production of complex geometries and prototypes.

  • The creation of the first artificial heart by Robert Jarvik in 1982, which paved the way for advanced medical technologies such as heart assist devices and replacement hearts.
  • The design of the first practical electric car by Thomas Davenport in 1834, which led to the development of modern electric vehicles and sustainable transportation solutions.
  • The invention of the first practical steam engine by James Watt in 1765, which powered the Industrial Revolution and transformed the way goods were produced.
  • The creation of the first smartphone by IBM engineer Frank Canova Jr. in 1992, which revolutionized personal communication and transformed the way we access information.

To recapitulate, inventive engineering means using your imagination and problem-solving skills to come up with new and innovative ideas. It’s like inventing something that has never been seen before, and it requires a lot of creativity and thinking outside the box. Thes examples above showcase how inventive engineering can lead to groundbreaking innovations that transform entire industries and improve the lives of people around the world.

On the other hand, creative engineering means taking existing ideas and finding new ways to use them or improve upon them. It’s like being an artist who takes paint and canvas and creates something new and unique. Creative engineering still requires imagination and creativity, but it doesn’t necessarily involve inventing something entirely new.

So in a way, inventive engineering is like creating a brand new toy that nobody has ever seen before, while creative engineering is like taking an existing toy and figuring out how to make it better or more fun to play with. Both are important and can be really fun!


“Inventivate” is not a commonly used word, but it can be a “mash-up”of “inventive” and “innovate”. “Inventivate” implies a focus on both creativity and innovation in engineering. In the context of Artificial Intelligence (AI), “inventivate” means using machine learning and other AI techniques to explore new possibilities and discover novel solutions to engineering challenges. “Inventivate” also involve leveraging AI to optimize existing processes and create more efficient and effective engineering solutions. “Inventivate” is a call to action to merge creativity and innovation in engineering practices and use AI as a tool to unlock new possibilities. Below we see the four dimensions of AI in the “inventivate” paradigm applied for the field of robotics. © Stelian Brad

Artificial Intelligence in Creative Engineering

Artificial intelligence (AI) is rapidly transforming the field of creative engineering, enabling engineers and designers to explore new possibilities and discover novel solutions to complex challenges. AI-powered tools and technologies are augmenting human creativity and problem-solving abilities, allowing for more efficient and effective design processes. From generative design to optimization algorithms, AI is enabling engineers to explore a much wider range of design possibilities and find optimal solutions much more quickly and efficiently than traditional design methods. With the ability to learn from data and adapt to changing circumstances, AI is unlocking new opportunities in fields such as product design, architecture, and fashion, and is transforming the way we approach creativity and innovation. Here we show some examples of AI applied in creative engineering.

AI in Creative Design: There are numerous examples of how AI is being used in creative design to generate new ideas, optimize designs, and improve workflow efficiency. For instance, the GPT-3 language model has been used to generate original designs for furniture, while neural networks have been used to optimize the aerodynamics of Formula 1 racing cars.

AI in Generative Art: AI is increasingly being used in generative art, which involves using algorithms to create new forms of artistic expression. For example, the artist Mario Klingemann has used neural networks to create original artwork that combines digital and traditional techniques.

AI in Music: AI is also being used to generate and manipulate music. For instance, Google’s Magenta project uses machine learning to generate original music compositions, while the OpenAI Jukebox can create music in the style of various artists.

AI in Film and Media: AI is increasingly being used in the film and media industries to create special effects, improve post-production workflows, and generate original content. For example, the movie “The Lion King” used AI to create photorealistic animal characters, while AI-generated news articles have been used by media outlets like the Washington Post.

AI in Architecture: AI is also being used in architecture to optimize designs and create new forms of building. For example, the AI firm Zaha Hadid Architects has used machine learning to optimize the design of the Nürburgring F1 circuit in Germany.

Artificial Intelligence in Inventive Engineering

Artificial intelligence (AI) has the potential to revolutionize inventive engineering by enabling engineers to solve complex problems and create innovative solutions in ways that were previously impossible. By leveraging AI techniques such as machine learning, engineers can analyze vast amounts of data and generate insights that can inform the design process. AI-powered generative design tools can also help engineers explore a much wider range of design possibilities and find optimal solutions much more quickly and efficiently than traditional design methods. As such, AI is increasingly becoming a valuable tool in the inventive engineering toolkit, allowing engineers to push the boundaries of what’s possible and create breakthrough solutions that can have a significant impact on society. We can indicate some inspirational applications of AI in inventive engineering.

AI in Generative Design: This involves using algorithms to automatically generate design alternatives for complex engineering systems, based on a set of constraints and design goals. An example of AI for generative design in robotics is the use of optimization algorithms to create optimal structures for robot components such as frames and joints, which can improve their strength, durability, and weight.

AI in Design Optimization: The use of machine learning to optimize engineering systems. Machine learning can also be used to predict the behavior of complex systems under different operating conditions, which can help engineers to optimize their designs. For example, researchers have used machine learning algorithms to optimize the design of aircraft wings, turbines, and other complex systems.

AI for Multi-Objective Optimization: This involves optimizing engineering systems with respect to multiple conflicting objectives, such as cost, performance, and sustainability. Multi-objective optimization is a challenging problem, but recent advances in AI and optimization techniques have shown promise in this area. Technical University of Denmark used AI and optimization techniques to develop a new approach to wind turbine design. They created a machine learning model that could predict the performance of different wind turbine designs based on a range of input parameters. They then used multi-objective optimization algorithms to search for the optimal design that achieved the best balance between energy output, cost, and environmental impact.

Future of Artificial Intelligence in Creative Engineering

Artificial Intelligence (AI) has been revolutionizing various fields, and the world of engineering is no exception. With the advancement of AI technologies, the future of engineering is bound to experience a significant transformation, particularly in the creative aspects. AI can help engineers to create, design, and build complex systems by automating the time-consuming and tedious tasks, which will result in a more efficient and effective design process. Furthermore, AI can assist engineers in finding new solutions and designs that may have been impossible to conceive otherwise, expanding the limits of what is possible. In the future, AI is expected to play a crucial role in the creative engineering process, enabling engineers to tackle more complex problems and ultimately leading to groundbreaking innovations. However, as with any rapidly evolving technology, ethical considerations and proper regulation must be put in place to ensure that AI is used for the benefit of society and does not have any negative consequences. Disruptive transformations of AI in creative design we foreseen are highlighted below:

  • AI integration in the creative process will become standard.
  • AI will revolutionize design and engineering decision making.
  • AI will make design more accessible to non-experts.
  • AI will enhance collaboration and communication in design teams.
  • AI will enable the creation of entirely new forms of products and systems.

Inspirational use cases:

  • Automotive design: AI can help engineers create innovative and efficient vehicle designs by generating and optimizing designs based on a set of parameters and constraints.
  • Architecture: AI can assist architects in designing energy-efficient buildings by analyzing environmental factors such as sunlight, wind patterns, and temperature, and generating designs that maximize energy efficiency.
  • Product design: AI can help designers create more personalized and customized products by using algorithms to generate design variations based on customer preferences and requirements.
  • Fashion design: AI can assist fashion designers in creating unique and innovative designs by generating new patterns and styles, and even predicting future fashion trends based on consumer data.
  • Aerospace engineering: AI can help aerospace engineers optimize the design of aircraft components and systems by using machine learning algorithms to analyze performance data and identify areas for improvement.
  • Medical device design: AI can assist in designing more effective and personalized medical devices by using data from patient records to generate optimized design solutions.

Future of Artificial Intelligence in Inventive Engineering

The future of artificial intelligence in inventive design is expected to bring significant advancements in various fields, including product design, architecture, and urban planning. AI algorithms and machine learning techniques can analyze vast amounts of data, including user preferences and behavior, to generate innovative design solutions. This can significantly reduce the time and cost involved in the design process and enable engineers and designers to focus on more complex tasks. Additionally, AI can facilitate collaboration between designers, engineers, and stakeholders, leading to more efficient and effective decision-making processes. As AI continues to evolve, it is likely that it will become an increasingly essential tool in inventive design, allowing for the creation of more innovative and sustainable solutions. Disruptive transformations in this direction would be the followings:

  • AI will transform the way we invent, design and create.
  • Autonomous systems will replace manual labor, streamline processes and reduce errors.
  • Smart machines will optimize designs and simulations for better products.
  • AI-generated concepts and innovations will open up new opportunities for growth and creativity.
  • AI will empower engineers to solve some of the most complex challenges facing humanity.
  • Inventive engineering with AI will lead us into the age of hyper-automation and beyond.

Inspirational uses cases:

  • Civil engineering: Designing and optimizing complex systems, such as smart buildings.
  • Mobility: Designing and optimizing complex systems, autonomous vehicles.
  • Materials: Innovating new materials and products with advanced functionalities, such as lightweight and durable 3D-printed structures or biodegradable packaging.
  • Digital twins: Creating digital twins to simulate and test systems in a virtual environment before deploying them in the real world.
  • Novel design: Collaborating with AI-powered creative tools to generate novel designs and concepts for emerging technologies and applications.

Application Domain: Future of Inventivate in Robotics

The future of robotics is looking brighter than ever before, thanks in part to the concept of “inventivate.” This term, which combines the words “inventive” and “innovate,” suggests a new way of thinking about engineering that emphasizes creativity, innovation, and the use of cutting-edge technology like artificial intelligence (AI). With the help of inventivate, we can expect to see robotics that are smarter, more adaptable, and more capable than ever before. AI will play a central role in this transformation, enabling robots to learn from their environment, make decisions, and even collaborate with humans in new and exciting ways. Whether it’s in manufacturing, healthcare, or any other field, inventivate in robotics promises to revolutionize the way we work and live, and usher in a new era of innovation and progress.

Disruptive transformations:

  • Enhanced automation and control: As AI continues to evolve, it will enable engineers to build smarter and more adaptable robots that can learn from their environment and interact with humans more effectively.
  • Augmenting human creativity and problem-solving abilities: The role of humans in the creative engineering and robotics process will remain crucial, with AI serving as a powerful tool to augment and enhance human creativity and problem-solving abilities.
  • Improving overall efficiency: AI-powered generative design is allowing engineers to explore a much wider range of design possibilities and find optimal solutions much more quickly and efficiently than traditional design methods.
  • Revolutionizing design and engineering decision making: AI will revolutionize design and engineering decision-making by providing new insights and information, enabling engineers to make better and more informed decisions.
  • Enabling new forms of products and systems: AI will enable the creation of entirely new forms of products and systems, opening up new possibilities for innovation and invention in the robotics field.

Inspirational use cases of AI in engineering and design of robotc system can provide insights into the potential of AI to revolutionize this field:

  • Automated design: Inventivate can be used to generate new design concepts and solutions for robotic systems, allowing engineers to quickly explore a wider range of possibilities and identify optimal solutions.
  • Adaptive control: Inventivate can be used to develop adaptive control algorithms that allow robots to learn and adapt to changing environments and tasks, improving their performance and versatility.
  • Collaborative robots: Inventivate can be used to develop new collaborative robots that can work safely and effectively alongside humans in a wide range of settings, such as manufacturing, healthcare, and logistics.
  • Intelligent sensors: Inventivate can be used to develop new sensors and sensor networks that enable robots to perceive and interact with their environment more effectively, improving their accuracy and responsiveness.
  • Autonomous navigation: Inventivate can be used to develop new navigation algorithms and systems that allow robots to move autonomously and safely in complex environments, such as factories, warehouses, and outdoor settings.
  • Human-robot interaction: Inventivate can be used to develop new algorithms and interfaces that enable robots to interact with humans more naturally and effectively, improving their usability and acceptance.

The Impact of AI on Robotics Through Inventivate

The integration of artificial intelligence (AI) and robotics has been one of the most exciting developments in engineering in recent years. The combination of these two cutting-edge technologies has the potential to revolutionize the way we live and work. One of the most promising approaches to AI and robotics is through the lens of “inventivate”. The impact of AI on robotics through inventivate is likely to be profound, as it will enable engineers and designers to explore new possibilities and create entirely new forms of products and systems that were previously impossible to achieve. Through the use of AI, robotics will become more accessible, efficient, and effective, leading to increased productivity, improved safety, and enhanced performance across a wide range of applications. From autonomous vehicles to smart factories and medical robotics, the possibilities for AI in robotics through inventivate are virtually limitless.

A methodoly for inventivate in robotics is the following one:

  • Define the problem: Clearly articulate the design problem to be solved and the constraints and requirements that need to be considered.
  • Creative design exploration: Conduct an initial exploration of creative design possibilities, leveraging brainstorming techniques, design thinking, and other creative approaches. This stage should allow for non-linear thinking and exploration of unexpected design solutions.
  • Inventive design refinement: Once a broad range of creative design solutions has been generated, select the most promising designs and refine them using inventive design techniques. Inventive design focuses on optimizing and improving the selected designs using principles such as TRIZ, biomimicry, and other problem-solving frameworks.
  • Artificial intelligence application: Develop and train machine learning models to generate multiple design solutions for the robot, considering different configurations, materials, and control strategies. The use of nonlinear algorithms, such as neural networks or genetic algorithms, can help identify unexpected design solutions that may not have been considered through traditional design methods. The choice of AI models would depend on the specific problem and data available. Some examples of AI models that could be used for generating multiple design solutions for a robot include:
    • Generative Adversarial Networks (GANs): GANs can be used to generate new and diverse design concepts by training a generator model to produce samples that are indistinguishable from real data, and a discriminator model that distinguishes between the generated and real data.
    • Variational Autoencoders (VAEs): VAEs can be used to generate new design solutions by learning a compressed representation of the data and then sampling from the learned distribution to generate new designs.
    • Evolutionary algorithms: Evolutionary algorithms such as Genetic Algorithms, Genetic Programming, or Evolution Strategies can be used to explore the space of possible design solutions by iteratively generating and evaluating designs and selecting the most promising ones for further mutation and reproduction.
  • AI-assisted design refinement: The machine learning-generated designs are then evaluated and refined using AI-assisted design refinement techniques. This stage may involve using computer simulations, virtual prototyping, and other advanced design tools to test and optimize the design solutions generated by the machine learning algorithms.
  • Iterative refinement: Iterate between steps 3 to 5 until a final design solution is achieved that meets the requirements and constraints of the problem definition. This iterative process allows for the incorporation of non-linear thinking and discovery of unexpected solutions at each stage.
  • Implementation and testing: Implement the final design solution and test its performance against the defined requirements and constraints.
  • Continuous improvement: Use feedback from the testing phase to improve the design and refine the methodology for future use.


credits: Stelian Brad [this material is based on the presentation “AI in Creative Engineering and Robotics: The Art of Engineering and Robotics: How AI is Revolutionizing Both”, Stelian Brad, 17 Febr. 2023, Technical University of Cluj-Napoca, in the context of EMERALD project: “Learning methods based on fundamental research using the methods of 3D printing with applicability in the field of bio-mechatronics”]