In a Nutshell …
🔥Trending in AI Science: Physical Reasoning
AI is moving beyond pattern recognition – toward capturing the laws of physics. From modeling planetary motion to simulating stars using physics-informed neural networks, researchers are exploring how AI can reason about the physical world to power breakthroughs in science and engineering.
💊 Diffusion Models in Drug Discovery
Diffusion models, popularly known for high-quality image generation, are now designing novel drug candidates. By capturing the complexity of molecular structures and optimizing for safety, stability, and effectiveness, they’re accelerating the path to faster, smarter drug discovery.
Deeper Dive
🔥Trending in AI Science: Physical Reasoning
What are scientific discoveries? They are abstract rules that generalize across countless observations. Newton’s law of motion applies to falling apples, orbiting moons, and flying rockets. A logistic regression model generalizes from data to predict outcomes. Large Language Models (LLMs) generalize across infinite amounts of text.
The scientific process starts with observations: for example, the moon changes shape and position in the sky. From repeated observations, we see patterns – the moon always follows a cycle. From these patterns, scientists summarize laws: the moon orbits Earth according to Newton’s laws of motion.
Generative models in AI can already capture complicated patterns in language, images, and video. But now researchers are asking a deeper question:
Can AI capture not just patterns, but the underlying physical laws of our universe?
If an AI model can generate videos of objects moving, can it also simulate the true physics – predicting how every object interacts? If yes, the applications could be profound: from accelerating spaceship design to making travel safer and cheaper – so perhaps one day, we really can vacation on the moon.
But here’s the challenge: bigger models don’t automatically learn physics.
- A Harvard–MIT study “What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models” tested whether models can predict planetary trajectories and gravitational forces.
- A ByteDance–Tsinghua study “How Far is Video Generation from World Model: A Physical Law Perspective” explored video generation of moving objects.
Both concluded: foundation models mimic training data patterns but fail to discover abstract physical rules.
The good news?
Enter Physics-Informed AI. Physics-Informed Neural Networks (PINNs), described in papers like “Differentiable Stellar Atmospheres with Physics-Informed Neural Networks” and “Sub-Sequential Physics-Informed Learning with State Space Model”, are neural networks that directly incorporate physical laws into their training. Instead of learning solely from data, the loss function also rewards models that respect established physical principles and penalizes those that violate them.
Similarly, “Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel” leverages dynamics models to predict causal effects grounded in physics, integrating them into Gaussian optimization via kernel functions. Put simply, it can find the best move in a game faster than other algorithms – by reasoning about what will happen next based on the laws of physics. This approach mirrors how humans intuitively anticipate outcomes in the real world.
Applications of Physical Reasoning
Predicting Earthquakes
“Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition” demonstrates how conditional diffusion models – popularly known for generating high-quality images – can be applied to geoscience. In this case, they generate realistic simulations of earthquake ground motion, supporting applications such as earthquake risk assessment, early warning systems, disaster preparedness and response.
Understanding How Stars Work
“Differentiable Stellar Atmospheres with Physics-Informed Neural Networks” asks: Can we determine a star’s temperature, composition, gravity, velocity, and internal structure from light-years away? Astronomers study the spectra of starlight to infer these properties. This paper uses a physics-informed neural network (PINN) to model the relationship more accurately, incorporating hydrostatic equilibrium as a physical constraint during training. The result brings us closer to understanding the inner workings of stars.
💊 Diffusion Models in Drug Discovery
Diffusion models – originally built for generative AI for high quality image generation – are now making waves in drug discovery.
They excel at:
- Capturing complex chemical distributions and molecular structures (see LDMol: A Text-to-Molecule Diffusion Model with Structurally Informative Latent Space).
- Allowing conditioned design, where scientists guide generation based on properties like binding affinity, safety, or solubility (see Generation of structure-guided pMHC-I libraries using Diffusion Models).
- Enabling multi-objective optimization—balancing many drug requirements at once (see PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion).
The result? Diffusion models can generate diverse, high-quality drug candidates faster and more efficiently than traditional trial-and-error methods – potentially speeding up the path from idea to treatment.
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