International Research Award on Computer Vision: October 2024

Tuesday, October 29, 2024

How AI Improves Quality Control with Computer Vision in Manufacturing

Artificial Intelligence (AI), especially computer vision, has brought advancements that help improve quality control in manufacturing. This combination enables the real-time inspection of items during production, increasing efficiency, accuracy, and consistency.

What is AI-Powered Quality Control?

AI-based quality control often relies on machine vision. This branch of AI processes visual information by utilizing cameras and smart algorithms. Machine vision analyzes images of products as they’re produced, detecting defects that human inspectors may miss. These AI models are trained using large image sets, allowing them to spot problems like surface flaws, incorrect sizes, or packaging errors. With this, manufacturers can ensure only high-standard products leave the factory.

A major advantage of AI-powered systems, compared to manual or automated ones, is their ability to learn and adapt. The more data these AI systems process, the better they become at spotting defects, cutting down on both false alarms and missed errors. This learning process helps manufacturers maintain quality across their products.

For those looking to enhance their role in this field, gaining the Certified Artificial Intelligence (AI) Expert™ credential can deepen your understanding and impact on production quality.

AI-Powered Automated Inspection

One of the areas seeing big changes from AI is automated optical inspection (AOI). Manufacturers have long depended on AOI systems to catch flaws, but older systems struggled with adapting to different production conditions. They followed preset rules and would fail when there were changes in the production process. AI has made these systems much more adaptable by allowing them to learn and adjust on the go.

These AI-based AOI systems use deep learning to evaluate large amounts of visual data, spotting even the tiniest flaws with precision. They get smarter after every inspection, cutting the need for manual checks and reducing errors. This results in smoother production lines and better product quality. For instance, Nissan’s assembly plant in Tennessee saw an improvement in defect detection rates by almost 7% using AI-powered inspections.

Real-Time Monitoring and Feedback

AI’s ability to monitor production lines in real-time brings another benefit. Traditional quality checks were done at intervals, but AI-driven systems constantly monitor operations. This immediate oversight helps catch mistakes as they occur, lowering the chances of defects going unnoticed. These systems don’t just detect problems; they also analyze production data, offering insights to optimize future processes. This helps reduce costly rework and downtime.

For example, Bosch uses AI to monitor data from vehicle parts during assembly. AI identifies potential problems early on and fixes them before they escalate, helping keep the assembly line running without hiccups.

Enhanced Visual Inspection with Computer Vision

AI has taken on a bigger role in visual quality inspections. Visual checks are crucial in fields like electronics, automotive, and aerospace, where detail is everything. Yet, manual inspections can take a lot of time and aren’t always accurate. AI-driven computer vision now automates this process, quickly detecting issues in surfaces, materials, and textures more efficiently than human inspectors.

In 2024, the BMW Group adopted AI-powered image recognition to inspect parts in real-time. AI compared images of components with thousands of samples to identify deviations, ensuring all parts meet quality standards before moving further down the line.

Edge computing has further enhanced these systems. With edge AI, data is processed directly on devices such as cameras or sensors. This speeds up feedback and lowers dependency on centralized servers, making operations more efficient, even in areas where network coverage might be unreliable.

Predictive Maintenance and AI’s Role

AI has also boosted quality control through predictive maintenance. Sensors fitted in production machines collect data like temperature and vibrations. AI analyzes this data to predict possible machine breakdowns, allowing operators to carry out repairs before they lead to defects or halts in production. Predictive maintenance is especially valuable in sectors like automotive and aerospace, where downtime is costly.

Siemens is an example of a company that uses AI for predictive maintenance. Their system monitors machinery and alerts staff when parts need attention, lowering the risk of unexpected downtime and saving on repair costs.

Applications Across Different Industries

Many manufacturers, from various industries, have embraced AI for quality control. The results are clear. Ford, for instance, uses AI in vehicle production, quickly identifying and fixing potential problems. This has cut rework costs and helped keep their standards high.

Another example is Teledyne e2v, which introduced an AI-powered imaging module in 2024. This new module offers more detailed inspections, including 3D depth data, which is essential for inspecting complex or layered products.

Challenges in AI Implementation

Although AI’s benefits in quality control are obvious, there are some challenges:Initial Costs: The investment in AI systems can be high, especially for hardware like high-quality cameras and AI software models.
Integration Issues: Incorporating AI into existing manufacturing lines isn’t always smooth. It often requires working with tech experts and giving workers special training.
Data Quality: AI relies on high-quality data to be effective. Poor or mislabeled data can lead to incorrect defect detection. Manufacturers need to put in place solid data collection and management systems to get the most from AI.

Conclusion

AI and computer vision have become crucial tools for improving quality control in manufacturing. They enable real-time defect detection, improve inspection accuracy, and offer insights that help manufacturers refine their processes. The growing shift towards edge computing also allows faster feedback and fewer issues related to connectivity. As more companies implement AI-powered solutions, quality control will continue to see improvements in reliability, waste reduction, and overall customer satisfaction.

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Computers normally can't see optical illusions — but a scientist combined AI with quantum mechanics to make it happen

A new artificial intelligence (AI) system can mimic how people interpret complex optical illusions for the first time, thanks to principles borrowed from the laws of quantum mechanics.

Optical illusions, such as the Necker Cube and Rubin's Vase, trick the brain into seeing one interpretation first and then another, as the image is studied. The human brain effectively switches between two or more different versions of what is possible, despite the image remaining static.

Computer vision, however, cannot simulate the psychological and neurological aspects of human vision and struggles to mimic our naturally evolved pattern recognition capabilities. The most advanced AI agents today, therefore, struggle to see optical illusions the way humans do.

But a new study published Aug. 22 in the journal APL Machine Learning demonstrated a technique that lets an AI imitate the way a human brain interprets an optical illusion, by utilizing the physical phenomenon of "quantum tunneling."

The AI system is dubbed a "quantum-tunneling deep neural network" and combines neural networks with quantum tunneling. A deep neural network is a collection of machine learning algorithms inspired by the structure and function of the brain — with multiple layers of nodes between the input and output. It can model complex non-linear relationships and, unlike conventional neural networks (which include a single layer between input and output) deep neural networks include many hidden layers.

Quantum tunneling, meanwhile, occurs when a subatomic particle, such as an electron or photon (particle of light), effectively passes through an impenetrable barrier. Because a subatomic particle like light can also behave as a wave — when it is not directly observed it is not in any fixed location — it has a small but finite probability of being on the other side of the barrier. When sufficient subatomic particles are present, some will "tunnel" through the barrier.

After the data representing the optical illusion passes through the quantum tunneling stage, the slightly altered image is processed by a deep neural network.

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Saturday, October 26, 2024

A Game-Changer for AI: The Tsetlin Machine’s Role in Reducing Energy Consumption

The rapid rise of Artificial Intelligence (AI) has transformed numerous sectors, from healthcare and finance to energy management and beyond. However, this growth in AI adoption has resulted in a significant issue of energy consumption. Modern AI models, particularly those based on deep learning and neural networks, are incredibly power-hungry. Training a single large-scale model can use as much energy as multiple households consume yearly, leading to significant environmental impact. As AI becomes more embedded in our daily lives, finding ways to reduce its energy usage is not just a technical challenge; it's an environmental priority.

The Tsetlin Machine offers a promising solution. Unlike traditional neural networks, which rely on complex mathematical computations and massive datasets, Tsetlin Machines employ a more straightforward, rule-based approach. This unique methodology makes them easier to interpret and significantly reduces energy consumption.

Understanding the Tsetlin Machine

The Tsetlin Machine is an AI model that reimagines learning and decision-making. Unlike neural networks, which rely on layers of neurons and complex computations, Tsetlin Machines use a rule-based approach driven by simple Boolean logic. We can think of Tsetlin Machines as machines that learn by creating rules to represent data patterns. They operate using binary operations, conjunctions, disjunctions, and negations, making them inherently simpler and less computationally intensive than traditional models.

TMs operate on the principle of reinforcement learning, using Tsetlin Automata to adjust their internal states based on feedback from the environment. These automata function as state machines that learn to make decisions by flipping bits. As the machine processes more data, it refines its decision-making rules to improve accuracy.

One main feature that differentiates Tsetlin Machines from neural networks is that they are easier to understand. Neural networks often work like “black boxes,” giving results without explaining how they got there. In contrast, Tsetlin Machines create clear, human-readable rules as they learn. This transparency makes Tsetlin Machines easier to use and simplifies the process of fixing and improving them.

Recent advancements have made Tsetlin Machines even more efficient. One essential improvement is deterministic state jumps, which means the machine no longer relies on random number generation to make decisions. In the past, Tsetlin Machines used random changes to adjust their internal states, which was only sometimes efficient. By switching to a more predictable, step-by-step approach, Tsetlin Machines now learn faster, respond more quickly, and use less energy.

The Current Energy Challenge in AI

The rapid growth of AI has led to a massive increase in energy use. The main reason is the training and deployment of deep learning models. These models, which power systems like image recognition, language processing, and recommendation systems, need vast amounts of data and complex math operations. For example, training a language model like GPT-4 involves processing billions of parameters and can take days or weeks on powerful, energy-hungry hardware like GPUs.

A study from the University of Massachusetts Amherst shows the significant impact of AI's high energy consumption. Researchers found that training a single AI model can emit over 626,000 pounds of CO₂, about the same as the emissions from five cars over their lifetimes. This large carbon footprint is due to the extensive computational power needed, often using GPUs for days or weeks. Furthermore, the data centers hosting these AI models consume a lot of electricity, usually sourced from non-renewable energy. As AI use becomes more widespread, the environmental cost of running these power-hungry models is becoming a significant concern. This situation emphasizes the need for more energy-efficient AI models, like the Tsetlin Machine, which aims to balance strong performance with sustainability.

There is also the financial side to consider. High energy use means higher costs, making AI solutions less affordable, especially for smaller businesses. This situation shows why we urgently need more energy-efficient AI models that deliver strong performance without harming the environment. This is where the Tsetlin Machine comes in as a promising alternative.

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Friday, October 25, 2024

The mainframe’s future in the age of AI

If there’s any doubt that mainframes will have a place in the AI future, many organizations running the hardware are already planning for it.

While the 60-year-old mainframe platform wasn’t created to run AI workloads, 86% of business and IT leaders surveyed by Kyndryl say they are deploying, or plan to deploy, AI tools or applications on their mainframes. Moreover, in the near term, 71% say they are already using AI-driven insights to assist with their mainframe modernization efforts.

Running AI on mainframes as a trend is still in its infancy, but the survey suggests many companies do not plan to give up their mainframes even as AI creates new computing needs, says Petra Goude, global practice leader for core enterprise and zCloud at global managed IT services company Kyndryl.

Many Kyndryl customers seem to be thinking about how to merge the mission-critical data on their mainframes with AI tools, she says. In addition to using AI with modernization efforts, almost half of those surveyed plan to use generative AI to unlock critical mainframe data and transform it into actionable insights.

“You either move the data to the [AI] model that typically runs in cloud today, or you move the models to the machine where the data runs,” she adds. “I believe you’re going to see both.”

Meanwhile, AI can also help companies modernize their mainframe strategies, whether it be assisting with moving workloads to the cloud, converting old mainframe code, or training workers in mainframe-related technologies, Goude says.

For most users, mainframe modernization means keeping some mission-critical workloads on premises while shifting other workloads to the cloud, Goude says. A huge majority of survey respondents plan to move some workloads off the mainframe, but nearly as many say they consider mainframes important to their business strategies.

Goude sees more business and IT leaders embracing a hybrid IT environment now than in past years, when many organizations were taking an all-or-nothing approach.

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Tuesday, October 22, 2024

Why Python is the language of choice for AI

The widespread adoption of AI is creating a paradigm shift in the software engineering world. Python has quickly become the programming language of choice for AI development due to its usability, mature ecosystem, and ability to meet the data-driven needs of AI and machine learning (ML) workflows. As AI expands to new industries and use cases, and Python’s functionality evolves, the demand for developers versed in the language will balloon. Python developers who invest in their AI and ML knowledge will be well-positioned to thrive in the era of AI.

Python is the most popular programming language, according to the TIOBE Programming Community Index. Python took its first lead over the other languages in 2021 and continued to explode in popularity as the growth of other languages largely remained stagnant. Meanwhile, nearly 30% of the searches for programming language tutorials on Google were for Python, nearly double the percentage for Java, which is ranked second, according to the PYPL Index, which is based on data from Google Trends. It’s no wonder that the popularity of Python has extended to AI workflows too.

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Monday, October 21, 2024

Implementing cross-validation for datasets with spatial autocorrelation using scikit-learn

A typical and useful assumption for statistical inference is that the data is independently and identically distributed (IID). We can take a random subset of patients and predict their likelihood for diabetes with a normal train-test split, no problem. In practice, however, there are some types of datasets where this assumption doesn’t hold, and a typical train-test split can introduce data leakage. When the distribution of the variable of interest is not random, the data is said to be autocorrelated — and this has implications on machine learning models.

We can find spatial autocorrelation on many datasets with a geospatial component. Consider the maps below:

If data were IID, it would look like the map on the right. But in real life, we have maps like on the left where we can easily observe patterns. The first law of geography states that nearer things are more related to each other than distant things. Attributes usually aren’t randomly distributed across a location – it’s more likely that an area is very similar to its neighbors. In the example above, the population level of a single area is likely to be similar of an adjacent area, as opposed to a distant one.
When do we need spatial cross-validation?

When data is autocorrelated, we might want to be extra wary about overfitting. In this case, if we use random samples for train-test splits or cross-validation, we violate the IID assumption since the samples are not statistically independent. Area A could be in the training set, but an Area Z in the validation set happens to be only a kilometer away from Area A while also sharing very similar features. The model would have a more accurate prediction for Area Z since it saw a very similar example in the training set. To fix this, grouping the data by area would prevent the model from peeking into data it shouldn’t be seeing. Here’s how spatial cross-validation would look like:

A good question to ask here: do we always want to prevent overfitting? Intuitively, yes. But as with most machine learning techniques, it depends. If it fits your use case, overfitting may even be beneficial!

Let’s say we had a randomly sampled national survey on wealth. We have wealth values of a distributed set of households across the country, and we’d like to infer the wealth levels for unsurveyed areas to get complete wealth data for the entire country. Here, the goal is only to fill in spatial gaps. Training with the data of the nearest areas would certainly help fill in the gaps more accurately!

It’s a different story if we were trying to build a generalizable model — say, one that we would apply to another country altogether. [2] In this case, exploiting the spatial autocorrelation property during training will likely inflate the accuracy of a potentially poor model. This is especially concerning if we use this seemingly-good model on an area where there is no ground truth for verifying.
Spatial cross-validation implementation on scikit-learn

To address this, we’d have to split areas between training and testing. If this were a normal train-test split, we could easily filter out a few areas out for our test data. In other cases, however, we would want to utilize all of the available data by using cross-validation. Unfortunately, scikit-learn’s built-in CV functions split the data randomly or by target variable, not by chosen columns. A workaround can be implemented, taking into consideration that our dataset includes geocoded elements.

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Saturday, October 19, 2024

The next phase of AI: Unlocking explainability with causal intelligence

Artificial intelligence is at a pivotal point in its evolution, moving into a new era that goes beyond simple pattern recognition to reasoning, and causal AI is at the forefront of this evolution.

Causal AI offers insights not just into what is happening, but why. This leap in decision-making intelligence has the potential to redefine the marketplace as businesses use these tools to enable smarter, more responsive processes. This next phase of AI evolution will shape the future of the AI ecosystem. Causal AI, unlike traditional models that rely on statistical patterns, is designed to provide explanations and reasoning, according to Scott Hebner, principal analyst at theCUBE Research.

“A lot of people talk about generative AI … but as a leader, you also have to be thinking ahead, particularly with AI, which is moving at an even faster pace than previous technological transformations,” Hebner said. “It’s important to take a futuristic view … so I’m doing a series of five papers about the advent of causal AI.”

Hebner spoke with theCUBE Research’s Principal Analyst Rob Strechay, during an AnalystANGLE segment on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how causal AI will enable a more structured approach, integrating large and small language models to build a more cohesive, responsive AI and machine learning ecosystem.
Understanding cause and effect

As organizations push the boundaries of AI, they are realizing that today’s models — particularly large language models — are effective at identifying patterns and making predictions but fall short in explaining the reasoning behind those predictions. LLMs operate on statistical probabilities, which are useful, but can be limiting in dynamic, ever-changing environments.

“Today’s predictive models and generative AI models that are embodied in the LLMs are pattern recognition machines. They operate on statistical probabilities … in a static world,” Hebner said. “What causal AI will tell you is how those statistical probabilities change when the world around you changes.”

Causal AI begins with agentic AI, which brings together AI agents in an ecosystem of AI large language models and domain-specific small language models to understand cause-and-effect relationships, a critical factor in helping humans problem-solve and make better decisions, as discussed in the article “The Causal AI Marketplace,” authored by Hebner.

Organizations are constantly in flux, and for AI to truly understand how the business operates, it needs to be able to understand cause and effect, according to Hebner. Why? Because in business everything is a cause and everything is an effect — and AI needs to keep up with that reality.

“Causal AI is all about helping people understand how the business operates. Then from there, it supports a dynamic world of change,” Hebner said. “It’s going to allow those statistical models, probabilities that traditional AI and machine learning operates upon, to adapt.”

The ability to simulate and test what-if scenarios based on the model is another benefit of causal AI. It offers businesses the flexibility to prescriptively model best-case outcomes impacting scenarios around profitability, customer retention and revenue.

“Today’s models are pretty good at predicting what you should do [and] forecast, and they generate the what, but they can’t tell you how it did it. And it certainly can’t tell you why this is the best answer,” Hebner said. “Causal AI is going to start to incrementally allow that explainability to be infused into these models, not only descriptively and predictively, but … prescriptively.”
The role of specialized AI models and agentive AI

While LLMs provide a general-purpose framework, small language models are designed for targeted tasks, allowing businesses to optimize AI for specific needs. These models ensure high data protection and specialized application.

“You need small language models that are specialized, secure and sovereign, that understand each of the domains within a business,” Hebner said.

He envisions a network of SLMs and LLMs where AI agents collaborate and contribute specific expertise. “The whole thing is going to come together in an architectural approach, and that’s going to represent the future,” he added.

This architectural approach will allow AI systems to interact with each other more effectively. LLMs will provide general knowledge, while SLMs focus on specific domains, creating a seamless flow of information, Hebner explained.

“We’re moving toward an ecosystem where AI agents teach each other, learn from each other and become smarter and smarter,” he added. “It’s going to be an architectural approach where agents work collaboratively, and that’s going to be key to the future.”

The case for causal AI

Causal AI isn’t just a concept on the horizon — it’s already gaining traction in industries that require a deeper level of decision intelligence. A recent Databricks Inc. and Dataiku Inc. survey of 400 AI professionals shows that over half of them are already using or experimenting with causal AI, which is expected to be one of the most adopted AI technologies in the coming year, according to Hebner.

“The number one technology that’s not being used today, but they plan to use over the next year, is causal AI,” Hebner said. “[Customers] want to build higher [return on investment] use cases, which require … reasoning, decision intelligence problem-solving and explainability.”

As the demand for more explainable and adaptable AI grows, causal AI will likely play an increasingly critical role in how businesses leverage artificial intelligence for better decision-making. The future of AI, according to Hebner, will be shaped by its ability to understand cause and effect. This shift could redefine how companies approach problem-solving and decision-making in an increasingly dynamic marketplace.

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Thursday, October 17, 2024

Transforming Computer Vision with AI and Generative AI

While conventional computer vision techniques were driven by manual feature extraction and classical algorithms to interpret images and videos, modern computer vision has been influenced by end-to-end deep learning models and generative AI (GenAI). This means greater possibilities for use cases like autonomous driving, object identification, and workplace safety.

By 2032, the global computer vision market size is projected to grow more than eight times from USD 20.31 billion to a whopping USD 175.72 billion.[1] The fast-evolving landscape of AI and computer vision is resulting in remarkably diverse applications across industries, such as camera-equipped patrol robots for the Singapore Police Force[2] and Abu Dhabi’s first multimodal Intelligent Transportation Central Platform, implemented as part of the capital’s urban transportation strategies.

AI-generative computer vision is an emerging field that focuses on creating or enhancing visual content through artificial intelligence, often employing techniques like deep learning, generative adversarial networks (GANs), and neural networks. It aims to generate new images, videos, or 3D models from scratch or based on input data, transforming the way visuals are designed, synthesized, and manipulated.

Key Aspects of AI-Generative Computer Vision:Generative Adversarial Networks (GANs): GANs are at the core of generative models, where two neural networks—the generator and the discriminator—work together to create realistic images by learning patterns in the data.

Image and Video Synthesis: AI models can create highly realistic images or even videos, often indistinguishable from real-world footage. This includes tasks like generating faces, scenes, or environments.

3D Model Generation: AI can assist in generating 3D models from 2D images or minimal input data, useful for applications like virtual reality, gaming, and architecture.

Image Inpainting and Super-Resolution: AI can fill in missing parts of images (inpainting) or enhance the resolution of low-quality images.

Style Transfer and Augmentation: AI can blend styles between different artworks or photos, allowing artists and designers to create unique visuals.

Applications:Entertainment and Media: AI-generated characters, animations, and special effects are used in movies, games, and virtual environments.

Healthcare: AI-generated medical images, like synthetic MRI scans, support training and diagnostic assistance.

Autonomous Vehicles: Generative models create simulated environments for training self-driving cars.
Design and Art: AI enhances creativity, enabling the design of new artworks, graphics, and fashion.

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Wednesday, October 16, 2024

Moving Beyond Data Collection to Data Orchestration

IoT connectivity and analytics are moving beyond data collection to data orchestration. Learn how real-time insights, edge analytics, and integrated data streams help optimize performance, reduce downtime, and drive smarter decisions, making data orchestration essential for industrial efficiency.

You could be forgiven for labeling IoT connectivity and analytics as tools primarily designed for data collection. These technologies do gather vast amounts of information from machines, sensors, and systems, but viewing them solely through the lens of collection limits their true potential. When companies fall into the trap of amassing data without a clear strategy for transforming it into actionable insights, it creates a pretty significant missed opportunity.

See also: How Industrial Connectivity and IoT Enable Manufacturing Digital Transformation

Without a coordinated approach, the data becomes noise. It overwhelms teams and leaves crucial insights buried in the clutter. This is where the shift from merely collecting data to orchestrating data comes into play. In a data orchestration model, IoT connectivity and analytics work together to synchronize operations. As a result, data isn’t just collected but dynamically analyzed and acted upon in real time.

By transitioning to a data orchestration approach, organizations move beyond passive data gathering and start seeing their data for what it really is—a chance to operate in a dynamic, holistic way with real-time guidance. This is where the world is heading, and this is the best opportunity to make a positive impact on operations.

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Tuesday, October 15, 2024

How Artificial Intelligence Is Decoding the Skies of Distant Worlds

Breakthrough in Exoplanet Atmosphere Analysis

Scientists from LMU, the ORIGINS Excellence Cluster, the Max Planck Institute for Extraterrestrial Physics (MPE), and the ORIGINS Data Science Lab (ODSL) have achieved a significant breakthrough in analyzing exoplanet atmospheres. By employing physics-informed neural networks (PINNs), they have enhanced the modeling of complex light scattering within these atmospheres, achieving greater precision than ever before. This innovative approach offers new insights into the role of clouds and could dramatically enhance our knowledge of distant worlds.

When distant exoplanets pass in front of their star, they block a small portion of the starlight, while an even smaller portion penetrates the planetary atmosphere. This interaction leads to variations in the light spectrum, which mirror the properties of the atmosphere such as chemical composition, temperature, and cloud cover.

To be able to analyze these measured spectra, however, scientists require models that are capable of calculating millions of synthetic spectra in a short time. Only by subsequently comparing the calculated spectra with the measured ones do we obtain information about the atmospheric composition of the observed exoplanets. And what is more, the highly detailed new observations coming from the James Webb Space Telescope (JWST) necessitate equally detailed and complex atmospheric models.
Enhanced Modeling With Physics-Informed Neural Networks

A key aspect of exoplanet research is the light scattering in the atmosphere, particularly the scattering off clouds. Previous models were unable to satisfactorily capture this scattering, which led to inaccuracies in the spectral analysis. Physics-informed neural networks offer a decisive advantage here, as they are capable of efficiently solving complex equations. In the just-published study, the researchers trained two such networks. The first model, which was developed without taking light scattering into account, demonstrated impressive accuracy with relative errors of mostly under one percent. Meanwhile, the second model incorporated approximations of so-called Rayleigh scattering – the same effect that makes the sky seem blue on Earth. Although these approximations require further improvement, the neural network was able to solve the complex equation, which represents an important advance.

Advantages of Interdisciplinary Collaboration

These new findings were possible thanks to a unique interdisciplinary collaboration between physicists from LMU Munich, the ORIGINS Excellence Cluster, the Max Planck Institute for Extraterrestrial Physics (MPE), and the ORIGINS Data Science Lab (ODSL), which specializes in the development of new AI-based methods in physics.

“This synergy not only advances exoplanet research, but also opens up new horizons for the development of AI-based methods in physics,” explains lead author of the study David Dahlbüdding from LMU. “We want to further expand our interdisciplinary collaboration in the future to simulate the scattering of light off clouds with greater precision and thus make full use of the potential of neural networks.”

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