# Neurosymbolic Mental Simulation and Imagination

## Introduction

A Neural-symbolic Framework for Mental Simulation by Michael Kissner, Simulation as an Engine of Physical Scene Understanding by Peter W. Battaglia, Jessica B. Hamrick and Joshua B. Tenenbaum, Analogues of Mental Simulation and Imagination in Deep Learning by Jessica B. Hamrick and Imagination Machines: A New Challenge for Artificial Intelligence by Sridhar Mahadevan.

## Model Learning

Interaction Networks for Learning about Objects, Relations and Physics by Peter Battaglia, Razvan Pascanu, Matthew Lai and Danilo J. Rezende, Visual Interaction Networks: Learning a Physics Simulator from Video by Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu and Andrea Tacchetti, Unsupervised Intuitive Physics from Visual Observations by Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra and Andrea Vedaldi, End-to-end Differentiable Physics for Learning and Control by Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum and J. Zico Kolter, Neural Relational Inference for Interacting Systems by Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling and Richard Zemel, Physics-as-inverse-graphics: Joint Unsupervised Learning of Objects and Physics from Video by Miguel Jaques, Michael Burke and Timothy Hospedales, Physics-as-inverse-graphics: Unsupervised Physical Parameter Estimation from Video by Miguel Jaques, Michael Burke and Timothy Hospedales, Flexible Neural Representation for Physics Prediction by Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li F. Fei-Fei, Josh Tenenbaum and Daniel L. Yamins, Learning Predictive Models from Observation and Interaction by Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine and Chelsea Finn and Causal Discovery in Physical Systems from Videos by Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox and Animesh Garg.

## Symbolic Regression

Deep Symbolic Regression: Recovering Mathematical Expressions from Data via Policy Gradients by Brenden K. Petersen, Learning Symbolic Physics with Graph Networks by Miles D. Cranmer, Rui Xu, Peter Battaglia and Shirley Ho, Discovering Symbolic Models from Deep Learning with Inductive Biases by Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel and Shirley Ho and AI Feynman: A Physics-inspired Method for Symbolic Regression by Silviu-Marian Udrescu and Max Tegmark.

## Episodic Working Memory

From Deep Learning to Episodic Memories: Creating Categories of Visual Experiences by Jigar Doshi, Zsolt Kira and Alan Wagner, Neural Episodic Control by Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adria Puigdomènech, Oriol Vinyals, Demis Hassabis, Daan Wierstra and Charles Blundell, Generalization of Reinforcement Learners with Working and Episodic Memory by Meire Fortunato, Melissa Tan, Ryan Faulkner, Steven Hansen, Adrià P. Badia, Gavin Buttimore, Charles Deck, Joel Z. Leibo and Charles Blundell and MEMO: A Deep Network for Flexible Combination of Episodic Memories by Andrea Banino, Adrià P. Badia, Raphael Köster, Martin J. Chadwick, Vinicius Zambaldi, Demis Hassabis, Caswell Barry, Matthew Botvinick, Dharshan Kumaran and Charles Blundell.

## Semantic Working Memory

Semantic Memory Modeling and Memory Interaction in Learning Agents by Wenwen Wang, Ah-Hwee Tan and Loo-Nin Teow.

## Visuospatial Working Memory

Neural Scene Representation and Rendering by S. M. Ali Eslami, Danilo J. Rezende, Frederic Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo, Avraham Ruderman, Andrei A. Rusu, Ivo Danihelka, Karol Gregor, David P. Reichert, Lars Buesing, Theophane Weber, Oriol Vinyals, Dan Rosenbaum, Neil Rabinowitz, Helen King, Chloe Hillier, Matt Botvinick, Daan Wierstra, Koray Kavukcuoglu and Demis Hassabis.

## Reinforcement Learning

Metacontrol for Adaptive Imagination-based Optimization by Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess and Peter W. Battaglia, Imagination-augmented Agents for Deep Reinforcement Learning by Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Rezende, Adria P. Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver and Daan Wierstra, Recurrent Environment Simulators by Silvia Chiappa, Sébastien Racaniere, Daan Wierstra and Shakir Mohamed and Recurrent World Models Facilitate Policy Evolution by David Ha and Jürgen Schmidhuber.

## Simulation

Computational Mechanics Enhanced by Deep Learning by Atsuya Oishi and Genki Yagawa, Physics-informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations by Maziar Raissi, Paris Perdikaris and George E. Karniadakis, Towards Finite Element Simulation Using Deep Learning by Francois Roewer-Despres, Najeeb Khan and Ian Stavness and Finite Element Network Analysis: A Machine Learning Based Computational Framework for the Simulation of Physical Systems by Mehdi Jokar and Fabio Semperlotti.

## Verification and Validation

Verification and Validation of Simulation Models by Robert G. Sargent.

## Design and Engineering

3D Design Using Generative Adversarial Networks and Physics-based Validation by Dule Shu, James Cunningham, Gary Stump, Simon W. Miller, Michael A. Yukish, Timothy W. Simpson and Conrad S. Tucker, A Physics-based Virtual Environment for Enhancing the Quality of Deep Generative Designs by Matthew Dering, James Cunningham, Raj Desai, Michael A. Yukish, Timothy W. Simpson and Conrad S. Tucker, Generative Design by Reinforcement Learning: Maximizing Diversity of Topology Optimized Designs by Seowoo Jang and Namwoo Kang, Synthesizing Designs with Interpart Dependencies Using Hierarchical Generative Adversarial Networks by Wei Chen and Mark Fuge, Design Space Exploration Using Constraint Satisfaction by Noel Titus and Karthik Ramani, Using Constraint Satisfaction for Designing Mechanical Systems by Pierre-Alain Yvars, Model Agnostic Solution of CSPs via Deep Learning: A Preliminary Study by Andrea Galassi, Michele Lombardi, Paola Mello and Michela Milano and Towards Effective Deep Learning for Constraint Satisfaction Problems by Hong Xu, Sven Koenig and T. K. Satish Kumar.

## Art

Rating Image Aesthetics Using Deep Learning by Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang and James Z. Wang, Reinforcement Learning for Generative Art by Jieliang Luo, Multi-criteria Reinforcement Learning by Zoltán Gábor, Zsolt Kalmár and Csaba Szepesvári, Evolving Art Using Multiple Aesthetic Measures by Eelco den Heijer and A. E. Eiben and Learning Aesthetic Judgements in Evolutionary Art Systems by Yang Li, Changjun Hu, Leandro L. Minku and Haolei Zuo.