PINN: A birthplace of Safe LLMs
Navin Manaswi
Authoring a GenAI book | LLM Researcher| Corporate Trainer| Published Author| Metaverse| LLM Agents | RAG | LLM Safety
Physics-Informed Neural Networks (PINNs) are poised to play a critical role in the advancement of both AI and Generative AI by bridging the gap between data-driven machine learning and the laws of physics. Traditional AI models often struggle with tasks that require an understanding of physical principles, relying solely on vast amounts of data. PINNs, on the other hand, seamlessly incorporate physical laws and constraints directly into their architecture, enabling them to make predictions and generate outputs that are not only accurate but also physically consistent. This unique ability empowers PINNs to excel in domains where data is scarce, noisy, or expensive to obtain, such as scientific simulations, engineering design, and healthcare. In the realm of Generative AI, PINNs have the potential to revolutionize the creation of realistic and physically plausible simulations, images, and videos, unlocking new possibilities for scientific discovery, creative expression, and practical applications.
Physics-Informed Neural Networks (PINNs) are a powerful approach that combines traditional machine learning methods with the known laws of physics, represented by mathematical models like the ones mentioned earlier (Navier-Stokes, Schr?dinger, Poisson's equations, etc.)
Accurate and consistent predictions: Domain-knowledge Mathematical models like Navier-Stokes or the Heat equation allow the incorporation of domain-specific knowledge into the AI model. This ensures that the neural network not only learns from the data but also respects the underlying physics of the problem. Hallucinations and bias can be reduced drastically.
Robust and Generalized Model: PINNs are less prone to overfitting because they adhere to the known physical laws described by the differential equations. This adherence to physics-based constraints helps the models generalize better across different conditions and scenarios
High Interpretability: Since PINNs use known physical laws, their predictions are easier to interpret in the context of established scientific knowledge. This interpretability is crucial for validating and trusting AI models in safety-critical applications
Higher Transparency: PINNs are versatile and can handle both forward (predicting system behavior from given conditions) and inverse problems (inferring unknown parameters or conditions from observations). This capability is crucial in many scientific and engineering applications.
Super Fast Results: Traditional numerical methods for solving PDEs can be computationally intensive and time-consuming. PINNs can provide real-time or near-real-time solutions, making them suitable for applications requiring fast decision-making. Generally safe LLMs take a longer time for deployment, PINN can solve this problem.
No dependency on Large data: Traditional neural networks often require large amounts of labelled data for training. PINNs can generalize well even with limited data by embedding physics equations directly into the model. This is particularly useful in domains where collecting labelled data is expensive or time-consuming.
To have a glimpse, we can go through top Mathematical Models under PINNs
Here are the top 5 Mathematical Models under PINNs ( Physics Inspired Neural Networks) --
Here are the equations for the requested physical models:
1. Navier-Stokes Equations
The Navier-Stokes equations describe the motion of viscous fluids.
ρ (?u/?t + u · ?u) = -?p + μ?2u + f
where:
?ρ/?t + ? · (ρu) = 0
2. Schr?dinger Equation
The Schr?dinger equation describes the wave function of a quantum-mechanical system.
i? ?Ψ/?t = ?Ψ
where:
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i: imaginary unit
?: reduced Planck constant
Ψ: wave function
t: time
?: Hamiltonian operator (represents the total energy of the system)
?Ψ = EΨ
where:
E: energy eigenvalue
3. Poisson's Equation
Poisson's equation relates the electric potential to the charge density.
?2φ = -ρ/ε?
where:
4. Heat Equation
The heat equation describes the diffusion of heat in a material.
?u/?t = α?2u
where:
5. Wave Equation
The wave equation describes the propagation of waves.
?2u/?t2 = c2?2u
where:
By incorporating PINNs into the LLM ( and in general GenAI) development and deployment pipeline, we can enhance the safety and trustworthiness of these powerful language models, mitigating the potential risks associated with their misuse or unintended consequences.
Authoring a GenAI book | LLM Researcher| Corporate Trainer| Published Author| Metaverse| LLM Agents | RAG | LLM Safety
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Supporting Convoy 2022
3 个月We are overlooking the main question: can science rely solely on induction? No, the Galilean revolution is precisely the realization that induction and deduction must be reconciled. Epistemologically, this necessary reconciliation must adhere to an imperative of falsifiability of hypotheses as advocated by Popper. The problem with machine learning (ML) is that it is purely inductive. It only infers from the particular to the general. The illusion that some ML models perform deduction—meaning they would be capable of inferring from the general to the particular—is... an epistemic illusion. They merely mimic pseudo-deduction by generalizing examples of syllogisms (like ChatGPT) or fragments of artificial data obeying physical laws (like PINNs). But do we see that this is not the same as logically representing an axiom or mathematically representing a physical law? The collapse of hypothetico-deductive logic is something we will pay dearly for in terms of collective intelligence, particularly in science. We are feeding data back into data, believing we are enriching the abstraction of models. But this is degenerate meta-empiricism, not empiricism.