# Invitation to a talk: Raban Iten, Fri. Nov. 2, 2018, 2 pm at the IQOQI seminar room

Discovering physical concepts with neural networks

The formalism of quantum physics is built upon that of classical mechanics. In principle, considering only experimental data without prior knowledge could lead to an alternative quantum formalism without conceptual issues like the measurement problem. As a first step towards finding such an alternative, we introduce a neural network architecture that models the physical reasoning process and can be used to extract physical concepts from experimental data in an unbiased way. We apply the neural network to a variety of simple physical examples in classical and quantum mechanics, like damped pendulums, two-particle collisions, and qubits. The network finds the physically relevant parameters, exploits conservation laws to make predictions, and can be used to gain conceptual insights. For example, given a time series of the positions of the Sun and Mars as observed from Earth, the network discovers the heliocentric model of the solar system - that is, it encodes the data into the angles of the two planets as seen from the Sun.

# Invitation to a talk: Raban Iten, Fri. Nov. 2, 2018, 2 pm at the IQOQI seminar room

Discovering physical concepts with neural networks

The formalism of quantum physics is built upon that of classical mechanics. In principle, considering only experimental data without prior knowledge could lead to an alternative quantum formalism without conceptual issues like the measurement problem. As a first step towards finding such an alternative, we introduce a neural network architecture that models the physical reasoning process and can be used to extract physical concepts from experimental data in an unbiased way. We apply the neural network to a variety of simple physical examples in classical and quantum mechanics, like damped pendulums, two-particle collisions, and qubits. The network finds the physically relevant parameters, exploits conservation laws to make predictions, and can be used to gain conceptual insights. For example, given a time series of the positions of the Sun and Mars as observed from Earth, the network discovers the heliocentric model of the solar system - that is, it encodes the data into the angles of the two planets as seen from the Sun.