Hey everyone! 👋

What are the possibilities of using AI and physics-informed learning methods to analyze physical data? 🤔

How can this approach help uncover physical laws, especially when data is random or sparse? ⚡

Would love to hear your thoughts! 💬

#Physics #DataScience #MachineLearning #PhysicsDiscovery

I’m not sure exactly what “physics-informed learning methods” are, but machine learning is already used to analyze data

There are many examples, and details in the footnotes:

https://en.m.wikipedia.org/wiki/Machine_learning_in_physics

If data are truly random, then there’s no pattern to discover. If data are sparse, the statistical uncertainty is large. I’m not sure how ML overcomes that. I think the strength of processing is in finding correlations in large amounts of data.

AI and physics informed learning methods can greatly enhance the analysis of physic data by extracting patterns, embedding physical laws, accelerating simulations and improving data reconstructions and prediction. They combine data driven intelligence with physical principles to make modeling, prediction and discovery more accurate and efficient.

I can certainly see the usefulness of computational methods for analyzing very large amounts of data, but in the case of sparse data with large associated error, I'd be more worried about the AI ( if not optimized properly ) 'finding' patterns where none exist, and going off on a 'wild goose chase'.

11 minutes ago, Aiaru Smat said:

Методы обучения, основанные на ИИ и физике, могут значительно улучшить анализ физических данных за счёт извлечения закономерностей, внедрения физических законов, ускорения моделирования и улучшения реконструкции данных и прогнозирования. Они сочетают интеллектуальные технологии, основанные на данных, с физическими принципами, делая моделирование, прогнозирование и исследование более точными и эффективными.

Thanks, that’s a very interesting explanation! Indeed, combining AI with physics clearly makes data analysis much more efficient and accurate.🙂

2 hours ago, Adila said:

Hey everyone! 👋

What are the possibilities of using AI and physics-informed learning methods to analyze physical data? 🤔

How can this approach help uncover physical laws, especially when data is random or sparse? ⚡

Would love to hear your thoughts! 💬

#Physics #DataScience #MachineLearning #PhysicsDiscovery

Hey! 👋
Great question! AI and physics-informed learning can really help in studying physical data. 🤖⚙️

By adding physics rules into machine learning models, we can get better results even if the data is noisy or small. The model “knows” what’s physically possible, so it avoids nonsense predictions.

This approach is already used in areas like fluid flow, materials, and space research. 🌌

It’s a cool way to mix data and theory to find new physical laws! 🔍

59 minutes ago, Sayora said:

By adding physics rules into machine learning models, we can get better results even if the data is noisy or small. The model “knows” what’s physically possible, so it avoids nonsense predictions.

This approach is already used in areas like fluid flow, materials, and space research. 🌌

You need to provide citations to support this. Pick at least one of those and provide a peer reviewed paper demonstrating the use of AI analytics on data which is sparse or noisy. How about fluid flow?

Several members have mentioned 'patterns'.

So what are patterns ?

What pattern is AI discoverable in the following data

1, 3, 13, 23, 53 ?

and is it the same pattern as

53, 23, 13, 3, 1 ?

What is the next number in the sequence: 1, 2, 4, 8, 16, ?

The answer is 31.

2 hours ago, TheVat said:

You need to provide citations to support this. Pick at least one of those and provide a peer reviewed paper demonstrating the use of AI analytics on data which is sparse or noisy. How about fluid flow?

A few red flags as @exchemist mentioned. "Hey that's a great question..."

"Thanks, that’s a very interesting explanation!"

Newbee talks to newbies in a dash jolly fashion after only a few hours of meeting, they have stuff in common, what are the chances!?

Hi! That’s a really good question
I think AI and physics-informed learning can be very powerful for analyzing complex or incomplete data.
In my research, I work with stellar light curves, and sometimes the data is noisy or has gaps — machine learning could definitely help identify patterns that traditional models might miss.
Combining AI with physical constraints sounds like a great way to make models both accurate and realistic!

6 hours ago, pinball1970 said:

A few red flags as @exchemist mentioned. "Hey that's a great question..."

"Thanks, that’s a very interesting explanation!"

Newbee talks to newbies in a dash jolly fashion after only a few hours of meeting, they have stuff in common, what are the chances!?

Yes. I occasionally reply as if these are real, to provide mods a little extra rope. We've an extended chat on the bot issues over in the Suggestion forum.

16 hours ago, Adila said:

Hey everyone! 👋

What are the possibilities of using AI and physics-informed learning methods to analyze physical data? 🤔

How can this approach help uncover physical laws, especially when data is random or sparse? ⚡

Would love to hear your thoughts! 💬

#Physics #DataScience #MachineLearning #PhysicsDiscovery

Hi!That’s a really cool topic! AI with physics-informed methods can help make sense of messy or limited data by using known physical laws.

It’s a smart way to get reliable results even when experiments are hard or data is noisy.

That’s a great topic! Physics-informed AI is becoming a powerful tool for analyzing complex or incomplete data. By embedding known physical laws directly into neural networks (like in Physics-Informed Neural Networks, PINNs), the model learns solutions that stay consistent with real physics rather than just fitting data. This helps a lot when data are noisy or sparse.

If you’re interested, check out Raissi et al. (2019) on PINNs and recent reviews on physics-informed machine learning— they give a solid foundation and practical examples across fluid dynamics, plasma physics, and astrophysics.

17 hours ago, Adila said:

Hey everyone! 👋

What are the possibilities of using AI and physics-informed learning methods to analyze physical data? 🤔

How can this approach help uncover physical laws, especially when data is random or sparse? ⚡

Would love to hear your thoughts! 💬

#Physics #DataScience #MachineLearning #PhysicsDiscovery

Hi there! 👋
That’s a really good question — AI and physics-informed learning are now used a lot in physics. For example, Raissi et al. (2019, Journal of Computational Physics) showed how Physics-Informed Neural Networks (PINNs) can solve partial differential equations even with sparse or noisy data. Another great overview is in Karniadakis et al. (2021, Nature Reviews Physics), where they discuss how combining physical laws with machine learning helps discover new models.

These methods are great for reconstructing hidden variables and learning governing equations directly from experimental data. Exciting field — I think it’s just the beginning! 🚀

That’s a really exciting question — combining AI with physics-informed learning opens up huge possibilities! ⚡

These methods let models respect physical laws (like conservation or symmetry) while still learning from data. So even when the dataset is noisy or limited, the model doesn’t just “guess” — it stays grounded in real physics.

This approach can help uncover hidden relationships in complex systems — for example, finding unknown parameters, reconstructing dynamics, or testing new hypotheses from experimental data.

In short, it bridges the gap between data-driven learning and physical understanding, turning AI into a real discovery tool rather than just a pattern recognizer. 🌌

New AI-based methods for analyzing physical data include Neural ODEs for modeling dynamics, Graph Neural Networks for structured interactions, physics-informed Gaussian Processes for uncertainty-aware predictions, Reinforcement Learning for discovering patterns and strategies, and Symbolic Regression (like AI Feynman or PySR) for deriving analytical formulas from data.