633 citations · 1.1k across the 9 of their papers we have counts for
10 papers
An Arrow of Time in Stable Autonomous Generation
Zhixin Lu
Does stable autonomous generation select a direction of time? This question matters both for learning models that autonomously generate prescribed dynamics and for designing contro…
Learning Continuous Chaotic Attractors with a Reservoir Computer
Lindsay M. Smith, Jason Z. Kim, Zhixin Lu +1
Neural systems are well known for their ability to learn and store information as memories. Even more impressive is their ability to abstract these memories to create complex inter…
Path-dependent Dynamics Induced by Rewiring Networks of Inertial Oscillators
William Qian, Lia Papadopoulos, Zhixin Lu +3
In networks of coupled oscillators, it is of interest to understand how interaction topology affects synchronization. Many studies have gained key insights into this question by st…
Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example
Jason Z. Kim, Zhixin Lu, Erfan Nozari +2
The ability to store and manipulate information is a hallmark of computational systems. Whereas computers are carefully engineered to represent and perform mathematical operations…
Supervised Chaotic Source Separation by a Tank of Water
Zhixin Lu, Jason Z. Kim, Danielle S. Bassett
Whether listening to overlapping conversations in a crowded room or recording the simultaneous electrical activity of millions of neurons, the natural world abounds with sparse mea…
Design of Large Sequential Conformational Change in Mechanical Networks
Jason Z. Kim, Zhixin Lu, Danielle S. Bassett
From the complex motions of robots to the oxygen binding of hemoglobin, the function of many mechanical systems depends on large, coordinated movements of their components. Such mo…
Invertible generalized synchronization: A putative mechanism for implicit learning in biological and artificial neural systems
Zhixin Lu, Danielle S. Bassett
Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that can learn to i…
Attractor Reconstruction by Machine Learning
Zhixin Lu, Brian R. Hunt, Edward Ott
A machine-learning approach called "reservoir computing" has been used successfully for short-term prediction and attractor reconstruction of chaotic dynamical systems from time se…
Conformational Control of Mechanical Networks
Jason Z. Kim, Zhixin Lu, Steven H. Strogatz +1
Understanding conformational change is crucial for programming and controlling the function of many mechanical systems such as allosteric enzymes and tunable metamaterials. Of part…
Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data
Jaideep Pathak, Zhixin Lu, Brian R. Hunt +2
We use recent advances in the machine learning area known as 'reservoir computing' to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic…