Discovering physical concepts with neural networks (1807.10300v3)

Abstract: Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy. Here, we approach this problem by modelling a neural network architecture after the human physical reasoning process, which has similarities to representation learning. This allows us to make progress towards the long-term goal of machine-assisted scientific discovery from experimental data without making prior assumptions about the system. We apply this method to toy examples and show that the network finds the physically relevant parameters, exploits conservation laws to make predictions, and can help to gain conceptual insights, e.g. Copernicus' conclusion that the solar system is heliocentric.

• The paper presents SciNet, a neural network that learns minimal, independent representations to infer underlying physical principles.
• It demonstrates versatility by accurately predicting dynamics in systems like damped pendulums, collision experiments, and quantum state tomography.
• The research challenges traditional scientific discovery by using a data-driven approach to generate novel hypotheses and validate established theories.

Insightful Overview of "Discovering Physical Concepts with Neural Networks"

The paper "Discovering Physical Concepts with Neural Networks" explores a novel approach to using neural networks for scientific discovery in physics, focusing on the automatic extraction of physical principles and parameters from experimental data. The authors attempt to model a neural network architecture on the process of human physical reasoning, which is notably aligned with principles of representation learning. This research aims to advance machine-assisted scientific discovery, particularly in instances where traditional prior assumptions about physical systems are not applicable.

Key Contributions

The paper outlines a method wherein a neural network, termed SciNet, is employed to discover physical concepts across various domains, including classical and quantum mechanics, without relying on pre-defined physical knowledge about the systems. Significant contributions include:

1. Interpretable Representations: The network is structured to learn minimal, sufficient, and uncorrelated representations of physical variables. This setup allows researchers to infer relevant physics concepts by evaluating how the system parameters are encoded.
2. General-Purpose Discovery Tool: Unlike most methods that are tailored to specific applications, SciNet is designed to be a versatile tool that can be applied to a wide range of physical problems, facilitating the discovery of principles across different scientific disciplines.
3. Evaluation on Toy Examples: The method is demonstrated through a series of illustrative examples:
   • Forecasting positions in a damped pendulum system, accurately identifying frequency and damping parameters.
   • Demonstrating conservation of angular momentum in a collision experiment.
   • Performing quantum tomography for one and two qubits, suggesting the system's ability to derive the dimension of underlying quantum states.
   • Predicting heliocentric positions within the solar system from geocentric observations, echoing historical insights of Copernican heliocentrism.

Methodological Insights

The methodology builds upon the concepts of encoder-decoder frameworks similar to autoencoders. The encoder compresses observations into latent representations, while the decoder reconstructs answers to posed questions concerning the physical system. Notably, the network architecture employs disentangling variational autoencoders to enforce the learning of independent latent variables, enhancing the interpretability of the learned model.

Results and Implications

The results demonstrate that SciNet efficiently predicts and extracts meaningful physical parameters and laws, such as dynamical equations and conservation laws, without prior knowledge of the systems. It implicitly verifies existing physical theories through learned representations and suggests the potential for discovering novel scientific concepts.

Practical Implications: This advancement posits neural networks as tools capable of replacing or augmenting traditional hypothesis-driven scientific inquiry. For practical applications, this suggests utilizing machine learning tools in experimental physics for hypothesis generation and validation.

Theoretical Implications: The work challenges conventional methods in scientific discovery by advocating for a data-driven approach free from human bias. This may provoke new ways of interpreting experimental data and developing physical theories.

Speculation on Future Developments

Looking forward, the research sets a trajectory for expanding neural network frameworks beyond their current scope. It highlights the potential development of AI systems as autonomous physicists, capable of formulating theories and generating insights without human input. Further, refining methods to handle correlations in data will enhance the robustness and application range of such networks.

The findings of this paper represent an essential step towards the broader vision of AI-assisted scientific endeavors, paving the way for future explorations where machine intelligence could substantially contribute to foundational breakthroughs in various scientific realms.