Although machine learning offers important new capabilities for solving today’s complex problems, more organizations may be tempted to apply machine learning techniques as a one-size-fits all solution.
The vast volumes of data and powerful computational processes now available to organizations has led some to apply machine learning indiscriminately, where it may not be efficient or even appropriate. To that point, MIT xPRO Machine Learning instructor Youssef Marzouk encourages engineers and scientists to have as much of an understanding about where machine learning can help, as where it can’t.
To use machine learning effectively, engineers and scientists need a clear understanding of the most common issues that machine learning can solve. In a recent MIT xPRO Machine Learning whitepaper titled “Applications For Machine Learning In Engineering and the Physical Sciences,” Professor Youssef Marzouk and fellow MIT colleagues outlined the potentials and limitations of machine learning in STEM.
Here are some common challenges that can be solved by machine learning:
Accelerate processing and increase efficiency Machine learning can wrap around existing science and engineering models to create fast and accurate surrogates, identify key patterns in model outputs, and help further tune and refine the models. All this helps more quickly and accurately predict outcomes at new inputs and design conditions.
Quantify and manage risk. Machine learning can be used to model the probability of different outcomes in a process that cannot easily be predicted due to randomness or noise. This is especially valuable for situations where reliability and safety are paramount.
Compensate for missing data. Gaps in a data set can severely limit accurate learning, inference, and prediction. Models trained by machine learning improve with more relevant data. When used correctly, machine learning can also help synthesize missing data that round out incomplete datasets.
Make more accurate predictions or conclusions from your data. You can streamline your data-to-prediction pipeline by tuning how your machine learning model’s parameters will be updated and learning during training. Building better models of your data will also improve the accuracy of subsequent predictions.
Solve complex classification and prediction problems. Predicting how an organism’s genome will be expressed or what the climate will be like in fifty years are examples of highly complex problems. Many modern machine learning problems take thousands or even millions of data samples (or far more) across many dimensions to build expressive and powerful predictors, often pushing far beyond traditional statistical methods.
Create new designs. There is often a disconnect between what designers envision and how products are made. It’s costly and time-consuming to simulate every variation of a long list of design variables. Machine learning can identify key variables, automatically generate good options, and help designers identify which best fits their requirements.
Increase yields. Manufacturers aim to overcome inconsistency in equipment performance and predict maintenance by applying machine learning to flag defects and quality issues before products ship to customers, improve efficiency on the production line, and increase yields by optimizing the use of manufacturing resources.
Machine learning is undoubtedly hitting its stride, as engineers and physical scientists leverage the competitive advantage of big data across industries — from aerospace, to construction, to pharmaceuticals, transportation, and energy. But it has never been more important to understand the physics-based models, computational science, and engineering paradigms upon which machine learning solutions are built.
The list above details the most common problems that organizations can solve with machine learning. For more specific applications across engineering and the physical sciences, download MIT xPRO’s free Machine Learning whitepaper.