How Modeling in Science is Changing with Machine Learning

Mathematical models help scientists understand how different systems work. For decades, scientists have used models and even broken down large systems like the ecosystem, climate and the universe, using statistical and theoretical models. Although models have helped scientists achieve great work in the past, models fail to capture detail and complexity in present-day data-rich environments.  

Machine Learning is changing that. With large datasets and sophisticated algorithms, researchers are able to examine systems that were too complex to model them previously. Thus, there is growing research to incorporate Machine Learning and Scientific Modeling to develop hybrid methods to gain greater accuracy in prediction, while yet maintaining scientific delineation of the model.

The new orientation is changing the way scientists solve complex problems.

The Drawbacks of Traditional Scientific Models

Traditional scientific models are largely descriptive and analytical, and so, they use a number of equations to show how different variables are interrelated. These equations are often drawn from empirical laws and established theories. This is the most applicable approach in simple systems, but, when it comes to highly complex and nonlinear systems, the approach is highly limited.

Complex systems, including financial markets, biological systems, climate systems, and large-scale physical systems, have many variables and need interaction between variables. In order to solve equations, variables need to be isolated, but in doing so the predictive capability is greatly decreased.

The discrepancy between what is theoretically possible and the complexity of reality is becoming increasingly more evident with the wealth of data scientific experiments and digital sensors are producing. In order to find the minute data patterns, researchers need new tools.

This is where machine learning comes in.

The Innovative Promise of Machine Learning

The essence of machine learning is its ability to find patterns within data autonomously. During training, machine learning algorithms define relationships on their own; therefore, researchers do not need to identify every relationship between the data.

This is especially powerful in high dimensional data. For example, deep learning systems can capture many complex relationships, and defining them with traditional equations may be virtually impossible.

In the past decade, machine learning has transformed many sectors, including: 

- Medical Diagnostics

- Climate Prediction

- Materials Science

- Particle Physics

- Drug Discovery and Genomics

In all of these areas, machine learning uncovers unnoticed patterns.

There are also some negative aspects in relying only on machine learning.

Benefits from Combining Data and Theory

Models based only on data often function as black boxes. While they can make some of the best predictions, they do not explain the reason behind the behaviors of the models.

In scientific studies, predicting behavior is often not sufficient. It is equally important to understand the reasons. After all, researchers have to ensure models comply with the requisite theoretical and physical constraints.

To address this, more and more scientists are resorting to hybrid modeling.

The principles of these models incorporate two main components:

1. Theories based on scientific principles and laws

2. Machine learning models that have the ability to detect and organize data based on existing relationships.

In Most instances, the machine learning part of a model is able to discover what traditional closed-form equations cannot; and the theory side of the model provides organization and interpretability.

Improving Prediction Accuracy

The main advantage of hybrid modeling is improved predictive accuracy. The theory enables scientists to make better predictions by integrating machine learning without losing the underlying theoretical model.

Models can help scientists make predictions about how a system will behave. Discrepancies between predicted behaviors and actual behaviors offer an opportunity for a machine learning model to be trained to make a better approximation.  

Scientists can, therefore, take advantage of machine learning through this flexibility without having to compromise their theoretical model.   

It can be enough to say this is why hybrid models outperform models with only data, and only models with an exclusive theoretical data source.  

Dealing with Unreliable and Noisy Data  

One of the biggest challenges to scientific research is managing uncertainty. Observational data is never perfect, and measurements can contain noise, errors, and missing data.  

Machine learning has built in methodologies to combat these challenges through:  

- Regularization processors to prevent overfitting  

- Cross validation to test how dependable a model is  

- Robust training to manage noisy data  

These methodologies ensure that models learn meaningful relationships and not just memorization of the training data.  

This frequently results in more reliable and consistent predictions.   

Machine learning also helps researchers to better understand the systems being studied in addition to improving predictions.

In examining the implications of a model, a scientist can find variables that are the most impactful, as well as the hidden data relationships that other variables may uncover. These unknown data relationships may help map out future lines of research.

Consider the example of a Machine Learning model that uncovers variables whose importance may have been overlooked. These variables are then given importance and can lead to the construction of experimental hypotheses.

In a way, machine learning should be seen as contributing to the active process of doing science rather than just the analytics of science.

Remaining challenges

There are challenges to integrating Machine Learning into the scientific method, and the challenges are plentiful.

Generalization, for example, is one of the biggest pitfalls that people encounter. Essentially the model should not be just memorizing patterns from the data set, but should be able to function properly for other data sets outside the training set.

This is why researchers need to validate their model against additional data sets, and employ rigorous criteria against their model.

There is also the issue of accessibility. While most scientists may be experts in their disciplines, they may not have prior experience with advanced machine learning techniques. For tools to be adopted, they will need to be highly intuitive, and to some degree, foster collaboration between disciplines.

The Future of Scientific Modeling

Provided that there is continued growth in computing capabilities and an augmented volume of available data, the application of machine learning in scientific modeling will likely become ubiquitous.

The primary function of machine learning is to advance the traditional scientific processes, and not supersede them. By combining theory with data-driven learning, researchers are able to develop models that are both accurate and easily interpretable.

This combined approach has the potential to significantly advance research across multiple domains, including biology, physics, economics, and environmental science.

Ultimately, the emphasis should be placed on advanced understanding instead of solely enhanced predictions. With the integration of machine learning into the scientific process, researchers have unparalleled capabilities to probe the complexity of the natural world and uncover new phenomena.