Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Le

Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Le

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Machine learning has revolutionized the way engineers approach problem-solving and decision-making processes. One of the latest advancements in this field is the development of physics-informed, explainable learning models. These models combine the power of machine learning with the fundamental principles of physics to create more accurate and interpretable models.

In this post, we will provide an introduction to physics-informed, explainable learning models for engineers. These models are designed to not only make accurate predictions, but also provide insights into the underlying physical processes driving the data.

Physics-informed learning models leverage the laws of physics to constrain the learning process, making the models more robust and reliable. By incorporating physical constraints into the learning process, these models can better capture the underlying dynamics of complex systems and make more accurate predictions.

In addition to being more accurate, physics-informed learning models are also more interpretable. This means that engineers can better understand and trust the predictions made by these models, leading to more informed decision-making.

Overall, physics-informed, explainable learning models offer a powerful tool for engineers to tackle complex problems and make more reliable predictions. By combining the power of machine learning with the principles of physics, engineers can create models that are not only accurate, but also interpretable and trustworthy.
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