T-7 - Revision history

Ros: /* Check out: key concepts and exercises */

2026-03-16T20:34:45Z

Check out: key concepts and exercises

← Older revision		Revision as of 22:34, 16 March 2026
Line 91:		Line 91:


	In <code>Exercise 13 </code>, you will see in which sense the trap model is a good effective model to describe a dynamics exploring a complicated energy landscape ~~with many metastable states~~, focusing on the REM landscape as an example.		In <code>Exercise 13 </code>, you will see in which sense the trap model is a good effective model to describe a dynamics exploring a complicated energy landscape, focusing on the REM landscape as an example.

	== To know more ==		== To know more ==

Ros: /* Problem 7: a simple model for aging */

2026-03-15T15:35:23Z

Problem 7: a simple model for aging

← Older revision		Revision as of 17:35, 15 March 2026
Line 30:		Line 30:

	[[File:Trap.png\|thumb\|right\|x160px\|Fig 6.3 - Traps in the trap model.]]		[[File:Trap.png\|thumb\|right\|x160px\|Fig 6.3 - Traps in the trap model.]]
	The trap model is an abstract model for the dynamics in complex landscapes ~~studied in <sup>[[#Notes\|[1] ]]</sup>~~. The configuration space is a collection of <math> M \gg 1 </math> traps labeled by <math> \alpha </math> (see sketch). The model is random, since each trap is associated to a random number <math> \tau_\alpha </math> that is called the mean trapping time. The dynamics is stochastic: it is a sequence of jumps between the traps, where the system spends in a trap <math> \alpha </math> a certain amount of time with mean value <math> \tau_\alpha</math>. This means that the probability to jump out of the trap in time <math> [t, t+dt]</math> is <math> dt/\tau_\alpha </math>. When the system exits the trap, it jumps into another one randomly chosen among the <math> M</math>. The mean trapping times are distributed as		The trap model is an abstract model for the dynamics in complex landscapes. The configuration space is a collection of <math> M \gg 1 </math> traps labeled by <math> \alpha </math> (see sketch). The model is random, since each trap is associated to a random number <math> \tau_\alpha </math> that is called the mean trapping time. The dynamics is stochastic: it is a sequence of jumps between the traps, where the system spends in a trap <math> \alpha </math> a certain amount of time with mean value <math> \tau_\alpha</math>. This means that the probability to jump out of the trap in time <math> [t, t+dt]</math> is <math> dt/\tau_\alpha </math>. When the system exits the trap, it jumps into another one randomly chosen among the <math> M</math>. The mean trapping times are distributed as
	<math display="block">		<math display="block">
	P_\mu(\tau)= \frac{\mu \tau_0^\mu}{\tau^{1+\mu}} \quad \quad \tau \geq \tau_0		P_\mu(\tau)= \frac{\mu \tau_0^\mu}{\tau^{1+\mu}} \quad \quad \tau \geq \tau_0
Line 43:		Line 43:


	~~<ol>~~
	<li> <em> Condensation.</em> Consider a dynamics running from time <math>t_w</math> to some later time <math> t_w+ t</math>: compute the typical value of the maximal trapping time <math> \tau_{\text{max}}(t) </math> encountered in this time interval, assuming that the system has spent exactly a time <math> \tau_\alpha </math> in each visited trap <math> \alpha </math>. Show that in the non-ergodic phase <math> \tau_{\text{max}}(t) \sim t </math>. Why is this interpretable as a condensation phenomenon?		<li> <em> Condensation.</em> Consider a dynamics running from time <math>t_w</math> to some later time <math> t_w+ t</math>: compute the typical value of the maximal trapping time <math> \tau_{\text{max}}(t) </math> encountered in this time interval, assuming that the system has spent exactly a time <math> \tau_\alpha </math> in each visited trap <math> \alpha </math>. Show that in the non-ergodic phase <math> \tau_{\text{max}}(t) \sim t </math>. Why is this interpretable as a condensation phenomenon?
	</li> <br>		</li> <br>

Ros: /* Problem 7: a simple model for aging */

2026-03-15T15:34:14Z

Problem 7: a simple model for aging

← Older revision		Revision as of 17:34, 15 March 2026
Line 30:		Line 30:

	[[File:Trap.png\|thumb\|right\|x160px\|Fig 6.3 - Traps in the trap model.]]		[[File:Trap.png\|thumb\|right\|x160px\|Fig 6.3 - Traps in the trap model.]]
	The trap model is an abstract model for the dynamics in complex landscapes studied in <sup>[[#Notes\|[1] ]]</sup>. The configuration space is a collection of <math> M \gg 1 </math> traps labeled by <math> \alpha </math> ~~having random depths/energies~~ (see sketch). The dynamics is a sequence of jumps between the traps: the system spends in a trap <math> \alpha </math> ~~an exponentially large~~ time with ~~average~~ <math> \tau_\alpha</math> (the probability to jump out of the trap in time <math> [t, t+dt]</math> is <math> dt/\tau_\alpha </math>.). When the system exits the trap, it jumps into another one randomly chosen among the <math> M</math>. The ~~average~~ times are distributed as		The trap model is an abstract model for the dynamics in complex landscapes studied in <sup>[[#Notes\|[1] ]]</sup>. The configuration space is a collection of <math> M \gg 1 </math> traps labeled by <math> \alpha </math> (see sketch). The model is random, since each trap is associated to a random number <math> \tau_\alpha </math> that is called the mean trapping time. The dynamics is stochastic: it is a sequence of jumps between the traps, where the system spends in a trap <math> \alpha </math> a certain amount of time with mean value <math> \tau_\alpha</math>. This means that the probability to jump out of the trap in time <math> [t, t+dt]</math> is <math> dt/\tau_\alpha </math>. When the system exits the trap, it jumps into another one randomly chosen among the <math> M</math>. The mean trapping times are distributed as
	<math display="block">		<math display="block">
	P_\mu(\tau)= \frac{\mu \tau_0^\mu}{\tau^{1+\mu}} \quad \quad \tau \geq \tau_0		P_\mu(\tau)= \frac{\mu \tau_0^\mu}{\tau^{1+\mu}} \quad \quad \tau \geq \tau_0
	</math>		</math>
	where <math> \mu </math> is a parameter. In this ~~exercise~~, we aim at understanding the main features of this dynamics. <br>		where <math> \mu </math> is a parameter. In this Problem, we aim at understanding the main features of this dynamics. <br>



	<ol>		<ol>
	<li> <em> Ergodicity breaking ~~and condensation~~.</em> Compute the average trapping time (averaging between the traps) and show that there is a critical value of <math> \mu </math> below which it diverges, signalling a non-ergodic phase (the system needs infinite time to explore the whole configuration space). Consider a dynamics running from time <math>t_w</math> to some later time <math> t_w+ t</math>: compute the typical value of the maximal trapping time <math> \tau_{\text{max}}(t) </math> encountered in this time interval, assuming that the system has spent exactly a time <math> \tau_\alpha </math> in each visited trap <math> \alpha </math>. ~~Show that in the non-ergodic phase <math> \tau_{\text{max}}(t) \sim t </math>. Why is this interpretable as a condensation phenomenon?~~		<li> <em> Ergodicity breaking.</em> Compute the average trapping time (averaging between the traps) and show that there is a critical value of <math> \mu </math> below which it diverges, signalling a non-ergodic phase: the system needs infinite time to explore the whole configuration space.
	</li> <br>		</li> <br>


	<li> <em>~~Aging and weak ergodicity breaking~~.</em> ~~Assume now that~~ the ~~trap represent a collection~~ of ~~microscopic configurations having self overlap~~ <math>q_{EA}</math>. ~~Assume~~ that the ~~overlap between configurations of different traps is~~ <math> ~~q_0~~ </math>. Justify why the correlation function can be written as		<ol>
			<li> <em> Condensation.</em> Consider a dynamics running from time <math>t_w</math> to some later time <math> t_w+ t</math>: compute the typical value of the maximal trapping time <math> \tau_{\text{max}}(t) </math> encountered in this time interval, assuming that the system has spent exactly a time <math> \tau_\alpha </math> in each visited trap <math> \alpha </math>. Show that in the non-ergodic phase <math> \tau_{\text{max}}(t) \sim t </math>. Why is this interpretable as a condensation phenomenon?
			</li> <br>


			<li> <em>Aging and weak ergodicity breaking.</em> Justify why the correlation function for this model can be written as
	<math display="block">		<math display="block">
	C(t_w + t, t_w)= ~~q_{EA}~~ \Pi(t, t_w~~)+ q_0 \left(1-\Pi(t, t_w)\right~~), \quad \quad \Pi(t, t_w)= \text{probability that systems has not jumped in }[t_w, t_w+t].		C(t_w + t, t_w)=\Pi(t, t_w), \quad \quad \Pi(t, t_w)= \text{probability that systems has not jumped in }[t_w, t_w+t].
	</math>		</math>
	In the non-ergodic regime, one finds:		In the non-ergodic regime, one finds:
Line 53:		Line 58:
	Why is this an indication of aging? Show that		Why is this an indication of aging? Show that
	<math display="block">		<math display="block">
	\lim_{t \to \infty} C(t_w + t, t_w)=~~q_0~~ \quad \text{ for finite }t_w, \quad \quad \lim_{t_w \to \infty} C(t_w + t, t_w)=~~q_{EA}~~ \quad \text{ for finite }t		\lim_{t \to \infty} C(t_w + t, t_w)=0 \quad \text{ for finite }t_w, \quad \quad \lim_{t_w \to \infty} C(t_w + t, t_w)=1 \quad \text{ for finite }t
	</math>		</math>
	~~When <math> q_0=0</math>, this~~ behaviour is called "weak ergodicity breaking". </li>		This behaviour is called "weak ergodicity breaking". </li>
	<br>		<br>

	<li>		<li>
	~~<code>Extra. </code>~~ <em>Power laws.</em> Study the asymptotic behavior of the correlation function for <math> t \ll t_w </math> and <math> t \gg t_w </math> and show that the dynamics is slow, characterized by power laws. </li>		<em>Power laws.</em> Study the asymptotic behavior of the correlation function for <math> t \ll t_w </math> and <math> t \gg t_w </math> and show that the dynamics is slow, characterized by power laws (algebraic behaviour). </li>
	</ol>		</ol>
	<br>		<br>

Ros: /* A dynamical dictionary: out-of-equilibrium, aging */

2026-03-15T15:29:01Z

A dynamical dictionary: out-of-equilibrium, aging

← Older revision		Revision as of 17:29, 15 March 2026
Line 11:		Line 11:


	[[File:Correlation Function.png\|thumb\|right\|x160px\|Fig 7.2 - Behaviour of the correlation function in a system displaying aging.]]		[[File:Correlation Function.png\|thumb\|right\|x160px\|Fig. 7 - Behaviour of the correlation function in a system displaying aging.]]
	<li> '''Equilibrating dynamics.''' A system evolving with thermal dynamics (e.g. Langevin dynamics) <ins> equilibrates dynamically </ins> if there is a timescale <math> \tau_{\text{eq}} </math> beyond which the dynamical trajectories sample the configurations of the system <math> \vec{\sigma} </math> with the frequency that is prescribed by the Gibbs Boltzmann measure, <math> \sim e^{-\beta E(\vec{\sigma})} </math>, where <math> \beta </math> is the inverse temperature associated to the noise. At equilibrium, one-point functions in time, like the energy of the system, reach a stationary value (the equilibrium value predicted by thermodynamics at that temperature), while two-point functions like the correlation function		<li> '''Equilibrating dynamics.''' A system evolving with thermal dynamics (e.g. Langevin dynamics) <ins> equilibrates dynamically </ins> if there is a timescale <math> \tau_{\text{eq}} </math> beyond which the dynamical trajectories sample the configurations of the system <math> \vec{\sigma} </math> with the frequency that is prescribed by the Gibbs Boltzmann measure, <math> \sim e^{-\beta E(\vec{\sigma})} </math>, where <math> \beta </math> is the inverse temperature associated to the noise. At equilibrium, one-point functions in time, like the energy of the system, reach a stationary value (the equilibrium value predicted by thermodynamics at that temperature), while two-point functions like the correlation function
	<math display="block">		<math display="block">
Line 21:		Line 21:


	<li> '''Out-of-equilibrium and aging.''' In some systems the equilibration timescale <math> \tau_{\text{eq}} </math> is extremely large/diverging with some parameter of the model (like <math>N</math>), and for very large time-scales the dynamics is <ins>out-of-equilibrium </ins>. In glassy systems, out-of-equilibrium dynamics is often characterized by <ins>aging</ins>: the relaxation timescale of a system (how slow the system evolves) depends on the age of the system itself (on how long the system has evolved so far). Aging can be seen in the behaviour of correlation function, see Fig 7.2: the timescale that the system needs to leave the plateau increases with the age of the system <math> t_w </math>, meaning that the system is becoming more and more slow as it gets more and more old.		<li> '''Out-of-equilibrium and aging.''' In some systems the equilibration timescale <math> \tau_{\text{eq}} </math> is extremely large/diverging with some parameter of the model (like <math>N</math>), and for very large time-scales the dynamics is <ins>out-of-equilibrium </ins>. In glassy systems, out-of-equilibrium dynamics is often characterized by <ins>aging</ins>: the relaxation timescale of a system (how slow the system evolves) depends on the age of the system itself (on how long the system has evolved so far). Aging can be seen in the behaviour of correlation function, see Fig 7: the timescale that the system needs to leave the plateau increases with the age of the system <math> t_w </math>, meaning that the system is becoming more and more slow as it gets more and more old.
	</li>		</li>
	<br>		<br>

Ros: /* A dynamical dictionary: energy barriers, out-of-equilibrium, aging */

2026-03-15T15:28:25Z

A dynamical dictionary: energy barriers, out-of-equilibrium, aging

← Older revision		Revision as of 17:28, 15 March 2026
Line 5:		Line 5:


	== A dynamical dictionary: ~~energy barriers,~~ out-of-equilibrium, aging ==		== A dynamical dictionary: out-of-equilibrium, aging ==


	<ul>		<ul>
	<li> '''Noise and Langevin dynamics.''' In problems 5 and 6 we have characterized the energy landscape of the spherical <math>p</math>-spin, and showed that it is made by plenty of stationary points where gradient descent can get stuck. In presence of noise,
	~~<math display="block">~~
	~~\frac{d \vec{\sigma}(t)}{dt}=- \nabla_\perp E(\vec{\sigma})+ \vec{\eta}(t), \quad \quad \langle \eta_i(t) \eta_j(t')\rangle= 2 T \delta_{ij} \delta(t-t')~~
	~~</math>~~
	~~the random terms kick the systems in random directions in configuration space, allowing to escape from stationary points.~~
	In Langevin dynamics, <math>\vec{\eta}(t)</math> a Gaussian vector at each time <math> t </math>, uncorrelated from the vectors at other times <math> t' \neq t </math>, with zero average and some constant variance proportional to temperature.
	~~</li>~~
	~~<br>~~


	<li> '''Activation and Arrhenius law.''' When the noise in the Langevin dynamics is weak (temperature is small), the dynamics does not get stuck in local minima forever, but for very large time. This time depends crucially on the <ins> energy barrier </ins> which separate the minimum from the other configurations (see Fig 6.1). The <ins>Arrhenius law</ins> states that the typical timescale <math> \tau</math> required to escape from a local minimum through a barrier of height <math> \Delta E </math> with thermal dynamics with inverse temperature <math> \beta </math> scales as <math>\tau \sim \tau_0 e^{-\beta \, \Delta E} </math>. A dynamics made of jumps from minimum to minimum through the crossing of energy barriers is called <ins> activated </ins>.
	~~</li>~~
	~~<br>~~

	[[File:Correlation Function.png\|thumb\|right\|x160px\|Fig 7.2 - Behaviour of the correlation function in a system displaying aging.]]		[[File:Correlation Function.png\|thumb\|right\|x160px\|Fig 7.2 - Behaviour of the correlation function in a system displaying aging.]]

Ros at 15:27, 15 March 2026

2026-03-15T15:27:57Z

← Older revision		Revision as of 17:27, 15 March 2026
Line 1:		Line 1:
	<strong>Goal: </strong> The goal of these problems is to understand some features of glassy dynamics (power laws, aging) in a simplified ~~single particle~~ description, the so called trap model.		<strong>Goal: </strong> The goal of these problems is to understand some features of glassy dynamics (power laws, aging) in a simplified description, the so called trap model.
	<br>		<br>
	<strong>Techniques: </strong> extreme value statistics, asymptotic analysis.		<strong>Techniques: </strong> extreme value statistics, asymptotic analysis.

Ros: /* A dynamical dictionary: energy barriers, out-of-equilibrium, aging */

2026-03-15T15:16:00Z

A dynamical dictionary: energy barriers, out-of-equilibrium, aging

← Older revision		Revision as of 17:16, 15 March 2026
Line 6:		Line 6:

	== A dynamical dictionary: energy barriers, out-of-equilibrium, aging ==		== A dynamical dictionary: energy barriers, out-of-equilibrium, aging ==
	~~[[File:Activated Jump.png\|thumb\|right\|x160px\|Fig 7.1 - Activated jump across an energy barrier.]]~~

	<ul>		<ul>

Ros: /* Problems */

2026-02-14T18:55:07Z

Problems

← Older revision		Revision as of 20:55, 14 February 2026
Line 78:		Line 78:

	<!--=== Problem 7.2: Motivating the model: from landscapes to traps ===		<!--=== Problem 7.2: Motivating the model: from landscapes to traps ===
	<!--and spherical <math>p</math>-spin model. While for the <math>p</math>-spin we think about Langevin dynamics, for the REM we consider Monte Carlo dynamics: at each time step the system in a given configuration <math> \vec{\sigma} </math> with energy <math> E_1 </math> tries to transition to another configuration that differs with respect to the previous one by a single spin flip; let the energy of this second configuration be <math> E_2 </math>. The transition occurs with probability one if <math> E_2 <E_1 </math>, and with probability <math> e^{-\beta (E_2- E_1)}</math> otherwise.~~-->~~		<!--and spherical <math>p</math>-spin model. While for the <math>p</math>-spin we think about Langevin dynamics, for the REM we consider Monte Carlo dynamics: at each time step the system in a given configuration <math> \vec{\sigma} </math> with energy <math> E_1 </math> tries to transition to another configuration that differs with respect to the previous one by a single spin flip; let the energy of this second configuration be <math> E_2 </math>. The transition occurs with probability one if <math> E_2 <E_1 </math>, and with probability <math> e^{-\beta (E_2- E_1)}</math> otherwise.
	<ol>		<ol>
	<li> <em> REM: distribution of depths of traps.</em> In the REM, the energy levels are independent Gaussian variables. In Lecture 1, we have shown that the Ground State <math> E_{\min} </math> has the statistics of <math> E_{\min }=E_{\min }^{\rm typ}+ \frac{1}{\sqrt{2 \log 2}}z </math>, with <math> z </math> Gumbel. The distribution <math> P_N^{\text{extrm}}(E) </math> of the smallest energies values <math> E_\alpha </math> among the <math> M=2^N </math> can be assumed to be the same. Show that:		<li> <em> REM: distribution of depths of traps.</em> In the REM, the energy levels are independent Gaussian variables. In Lecture 1, we have shown that the Ground State <math> E_{\min} </math> has the statistics of <math> E_{\min }=E_{\min }^{\rm typ}+ \frac{1}{\sqrt{2 \log 2}}z </math>, with <math> z </math> Gumbel. The distribution <math> P_N^{\text{extrm}}(E) </math> of the smallest energies values <math> E_\alpha </math> among the <math> M=2^N </math> can be assumed to be the same. Show that:

Ros: /* Problem 7: a simple model for aging */

2026-02-14T18:54:43Z

Problem 7: a simple model for aging

← Older revision		Revision as of 20:54, 14 February 2026
Line 78:		Line 78:

	<!--=== Problem 7.2: Motivating the model: from landscapes to traps ===		<!--=== Problem 7.2: Motivating the model: from landscapes to traps ===


	<!--and spherical <math>p</math>-spin model. While for the <math>p</math>-spin we think about Langevin dynamics, for the REM we consider Monte Carlo dynamics: at each time step the system in a given configuration <math> \vec{\sigma} </math> with energy <math> E_1 </math> tries to transition to another configuration that differs with respect to the previous one by a single spin flip; let the energy of this second configuration be <math> E_2 </math>. The transition occurs with probability one if <math> E_2 <E_1 </math>, and with probability <math> e^{-\beta (E_2- E_1)}</math> otherwise.-->		<!--and spherical <math>p</math>-spin model. While for the <math>p</math>-spin we think about Langevin dynamics, for the REM we consider Monte Carlo dynamics: at each time step the system in a given configuration <math> \vec{\sigma} </math> with energy <math> E_1 </math> tries to transition to another configuration that differs with respect to the previous one by a single spin flip; let the energy of this second configuration be <math> E_2 </math>. The transition occurs with probability one if <math> E_2 <E_1 </math>, and with probability <math> e^{-\beta (E_2- E_1)}</math> otherwise.-->


	<ol>		<ol>
	<li> <em> REM: distribution of depths of traps.</em> In the REM, the energy levels are independent Gaussian variables. In Lecture 1, we have shown that the Ground State <math> E_{\min} </math> has the statistics of <math> E_{\min }=E_{\min }^{\rm typ}+ \frac{1}{\sqrt{2 \log 2}}z </math>, with <math> z </math> Gumbel. The distribution <math> P_N^{\text{extrm}}(E) </math> of the smallest energies values <math> E_\alpha </math> among the <math> M=2^N </math> can be assumed to be the same. Show that:		<li> <em> REM: distribution of depths of traps.</em> In the REM, the energy levels are independent Gaussian variables. In Lecture 1, we have shown that the Ground State <math> E_{\min} </math> has the statistics of <math> E_{\min }=E_{\min }^{\rm typ}+ \frac{1}{\sqrt{2 \log 2}}z </math>, with <math> z </math> Gumbel. The distribution <math> P_N^{\text{extrm}}(E) </math> of the smallest energies values <math> E_\alpha </math> among the <math> M=2^N </math> can be assumed to be the same. Show that:
Line 91:		Line 87:
	</center>		</center>
	(Hint: approximate the Gumbel distribution for small argument). </li> <br>		(Hint: approximate the Gumbel distribution for small argument). </li> <br>


	<li> <em> REM: trapping times.</em> The Arrhenius law states that the time needed for the system to escape from a trap of energy density <math> \epsilon<0 </math> and reach a configuration of zero energy density is <math> \tau \sim e^{-\beta N \epsilon} </math>. This is a trapping time. Given the energy distribution <math> P_N^{\text{extrm}}(E) </math>, determine the distribution of trapping times <math> P_\mu(\tau) </math>: what plays the role of <math> \mu</math>? Is the non-ergodic transition in the TRAP model consistent with what we know about the REM? </li><br>		<li> <em> REM: trapping times.</em> The Arrhenius law states that the time needed for the system to escape from a trap of energy density <math> \epsilon<0 </math> and reach a configuration of zero energy density is <math> \tau \sim e^{-\beta N \epsilon} </math>. This is a trapping time. Given the energy distribution <math> P_N^{\text{extrm}}(E) </math>, determine the distribution of trapping times <math> P_\mu(\tau) </math>: what plays the role of <math> \mu</math>? Is the non-ergodic transition in the TRAP model consistent with what we know about the REM? </li><br>

	<li> <em> Extra: p-spin and the “trap” picture.</em> In Problems 6, we have seen that the energy landscape of the spherical <math>p</math>-spin is characterized by the threshold energy, below which plenty of minima appear. Explain why the trap model corresponds to the following picture for the dynamics: the system is trapped into minima below the threshold for exponentially large times, and then jumps from minimum to minimum passing through the threshold energy.		<li> <em> Extra: p-spin and the “trap” picture.</em> In Problems 6, we have seen that the energy landscape of the spherical <math>p</math>-spin is characterized by the threshold energy, below which plenty of minima appear. Explain why the trap model corresponds to the following picture for the dynamics: the system is trapped into minima below the threshold for exponentially large times, and then jumps from minimum to minimum passing through the threshold energy.
	</li>		</li>

Ros: /* Problem 7.2: Motivating the model: from landscapes to traps */

2026-02-14T18:54:14Z

Problem 7.2: Motivating the model: from landscapes to traps

← Older revision		Revision as of 20:54, 14 February 2026
Line 77:		Line 77:
	<!---->		<!---->

	=== Problem 7.2: Motivating the model: from landscapes to traps ===		<!--=== Problem 7.2: Motivating the model: from landscapes to traps ===


Line 98:		Line 98:
	</li>		</li>
	</ol>		</ol>
	<br>		<br>-->

	== Check out: key concepts and exercises ==		== Check out: key concepts and exercises ==