Machine learning models for physics and engineering

A model is a representation of some relevant parts of reality. A statue is a model of a person, a word is a model of a thing. In physics, we are interested in mathematical models — equations describing the world. These models help us understand phenomena and predict their behavior. Models are a simplification of reality, based on assumptions. As long as the assumptions are sound, model predictions are expected to be accurate. Force = mass x acceleration is a famous model that works very well for many everyday scenarios but fails for tiny objects (electrons), massive objects (black holes), or fast objects (photons).

Accuracy and simplicity are the two important factors for evaluating models. Most of the time there is a trade-off between the two. A city map drawn to a scale of 1:1 can be very accurate but very complex, hence totally useless. Therefore for a model to be useful, it should be above a certain accuracy and below a certain complexity where the specifications depend on the application.

As an example, let’s look at 3 models for the shape of our Earth: Flat, Sphere, Oblate spheroid (Figure 1). As accuracy increases, the computational complexity of the model increases.

Figure 1 Three different models for the shape of the Earth.

Choosing which model to use depends very much on the application. Even though we are not “flat earthers”, we actually consider the Earth as “flat” for many of our day-to-day activities such as going to the supermarket. But for many other activities like launching a satellite, we need more accurate models and we can afford more computational complexity. Our application specifies the shaded blue area we want our models to be in (Figure 2).

Figure 2 Accepted mod el spaces (shaded blue areas) for two different applications. Note that for going to the supermarket, the constraint is an upper bound on computational complexity, while for launching a satellite, the constraint is a lower bound on accuracy.

We frequently experience situations where our physics models are either not accurate enough (Model X, Fig. 3) or computationally too complex (Model Y, Fig. 3) to satisfy our application (Blue shaded area, Fig. 3). Machine learning can help in two fundamental ways (Figure 3): (1) It can improve accuracy, without increasing the complexity. (2) It can decrease complexity without a loss of accuracy. Let’s see some examples for both.

Figure 3 Two physics-based models X and Y out of specification. Two ways machine learning can help (1) by increasing accuracy or (2) by decreasing computational complexity.

In one of our recent projects on building a novel particle size analyzer, the particle scattering model based on Mie theory was not accurate enough for the application. Using a machine learning model (random forests) we were able to build a model for particle size prediction that satisfied the specifications. You can read more here in our paper. In this case, we were able to increase model accuracy without increasing computational complexity (Figure 3, (1)). In another project related to microscopy, the image reconstruction algorithm based on physical diffraction laws took around 10 minutes to run, hindering the ability of the microscope to provide “live” images. We helped our collaborators by developing a deep learning model that can achieve the same construction in less than 1 second. Here, we were able to simplify the computational complexity of the calculation, while not sacrificing any accuracy (Figure 3, (2)).

#engineering #machine-learning #physics #data-science

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• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

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• Rise of Robots

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Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Machine Learning can analyze millions of data sets and recognize patterns within minutes. While we know what Machine Learning is and what it does, there’s little that is known about the different Machine Learning models types. Algorithms and models form the basis for Machine Learning programs and applications that enterprises use for their technical projects.
There are multiple types of Machine Learning models. Each model provides specific instructions to the program for executing a task. In general, there are three different techniques under which different models can be classified.

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