316 citations · 2.3k across the 42 of their papers we have counts for
15 papers · 1 filter
On the Information Required for Feedback Control
Matthew P. Leighton, Jose M. Betancourt, Thierry Emonet +2
Biological systems across scales, along with many engineering problems, must control noisy systems with limited information. Here we study information-limited feedback control of s…
Information geometry for multiparameter models: New perspectives on the origin of simplicity
Katherine N. Quinn, Michael C. Abbott, Mark K. Transtrum +2
Complex models in physics, biology, economics, and engineering are often sloppy, meaning that the model parameters are not well determined by the model predictions for collective b…
Thermodynamic stability and critical points in multicomponent mixtures with structured interactions
Isabella R. Graf, Benjamin B. Machta
Theoretical work has shed light on the phase behavior of idealized mixtures of many components with random interactions. But typical mixtures interact through particular physical f…
Irreversibility in dynamical phases and transitions
Daniel S. Seara, Benjamin B. Machta, Michael P. Murrell
Living and non-living active matter consumes energy at the microscopic scale to drive emergent, macroscopic behavior including traveling waves and coherent oscillations. Recent wor…
Energy Dissipation Bounds for Autonomous Thermodynamic Cycles
Samuel J. Bryant, Benjamin B. Machta
How much free energy is irreversibly lost during a thermodynamic process? For deterministic protocols, lower bounds on energy dissipation arise from the thermodynamic friction asso…
A scaling law from discrete to continuous solutions of channel capacity problems in the low-noise limit
Michael C. Abbott, Benjamin B. Machta
An analog communication channel typically achieves its full capacity when the distribution of inputs is discrete, composed of just K symbols, such as voltage levels or wavelengths.…
Information loss under coarse-graining: a geometric approach
Archishman Raju, Benjamin B. Machta, James P. Sethna
We use information geometry, in which the local distance between models measures their distinguishability from data, to quantify the flow of information under the renormalization g…
A dissipation bound for thermodynamic control
Benjamin B. Machta
Biological and engineered systems operate by coupling function to the transfer of heat and/or particles down a thermal or chemical gradient. In idealized \textit{deterministically}…
Sloppiness and Emergent Theories in Physics, Biology, and Beyond
Mark K. Transtrum, Benjamin Machta, Kevin Brown +3
Large scale models of physical phenomena demand the development of new statistical and computational tools in order to be effective. Many such models are `sloppy', i.e., exhibit be…
Universal scaling function for the two-dimensional Ising model in an external field: A pragmatic approach
Yan-Jiun Chen, Natalie M. Paquette, Benjamin B. Machta +1
We report an effective functional form for the spin-spin correlation function of the 2D Ising model as a function of temperature and field. Although the Ising model has been well s…
Parameter Space Compression Underlies Emergent Theories and Predictive Models
Benjamin B. Machta, Ricky Chachra, Mark K. Transtrum +1
We report a similarity between the microscopic parameter dependance of emergent theories in physics and that of multiparameter models common in other areas of science. In both case…
Experimental observations of dynamic critical phenomena in a lipid membrane
Aurelia R. Honerkamp-Smith, Benjamin B. Machta, Sarah L. Keller
Near a critical point, the time scale of thermally-induced fluctuations diverges in a manner determined by the dynamic universality class. Experiments have verified predicted 3D dy…
The geometry of nonlinear least squares with applications to sloppy models and optimization
Mark K. Transtrum, Benjamin B. Machta, James P. Sethna
Parameter estimation by nonlinear least squares minimization is a common problem with an elegant geometric interpretation: the possible parameter values of a model induce a manifol…
Why are nonlinear fits so challenging?
M. K. Transtrum, B. B. Machta, J. P. Sethna
Fitting model parameters to experimental data is a common yet often challenging task, especially if the model contains many parameters. Typically, algorithms get lost in regions of…
Parallel Dynamics and Computational Complexity of Network Growth Models
Benjamin Machta, Jonthan Machta
The parallel computational complexity or depth of growing network models is investigated. The networks considered are generated by preferential attachment rules where the probabili…