global attractors for gradient flows in metric spaces · global attractors for gradient ﬂows in...

Evolution equations and the Wasserstein metric Curves of Maximal Slope Existence of a Global Attractor Applications

Global attractors for gradient flowsin metric spaces

Riccarda Rossi(University of Brescia)

in collaboration withAntonio Segatti (WIAS, Berlin),

Ulisse Stefanelli (IMATI–CNR, Pavia)

Recent advances in Free Boundary Problems and related topics

Levico, September 14–16 2006

Riccarda Rossi

Global attractors for gradient flows in metric spaces

Evolution PDEs of diffusive type and theWasserstein metric

∂tρ− div(

ρ∇(δLδρ

)= 0 (x , t) ∈ Rn × (0,+∞),

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∂tρ− div(

ρ∇(δLδρ

)= 0 (x , t) ∈ Rn × (0,+∞),

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∂tρ− div(

ρ∇(δLδρ

)= 0 (x , t) ∈ Rn × (0,+∞),

L(ρ) :=

L(x , ρ(x),∇ρ(x))dx (Integral functional)

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∂tρ− div(

ρ∇(δLδρ

)= 0 (x , t) ∈ Rn × (0,+∞),

L(ρ) :=

L = L(x , ρ,∇ρ) : Rn × (0,+∞)× Rn → R (Lagrangian)

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∂tρ− div(

ρ∇(δLδρ

)= 0 (x , t) ∈ Rn × (0,+∞),

L(ρ) :=

δLδρ

= ∂ρL(x , ρ,∇ρ)− div(∂∇ρL(x , ρ,∇ρ))

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∂tρ− div

(ρ∇( δL

δρ ))

= 0 (x , t) ∈ Rn × (0,+∞),

ρ(x , t) ≥ 0,∫

Rn ρ(x , t) dx = 1 ∀ (x , t) ∈ Rn × (0,+∞),∫Rn |x |2ρ(x , t) dx < +∞ ∀ t ≥ 0,

L(ρ) :=

δLδρ

= ∂ρL(x , ρ,∇ρ)− div(∂∇ρL(x , ρ,∇ρ))

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∂tρ− div

(ρ∇( δL

δρ ))

= 0 (x , t) ∈ Rn × (0,+∞),

ρ(x , t) ≥ 0,∫

For t fixed, identify ρ(·, t) with the probability measure µt := ρ(·, t)dx

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∂tρ− div

(ρ∇( δL

δρ ))

= 0 (x , t) ∈ Rn × (0,+∞),

ρ(x , t) ≥ 0,∫

For t fixed, identify ρ(·, t) with the probability measure µt := ρ(·, t)dx

then L can be considered defined on P2(Rn) (the space of proba-bility measures on Rn with finite second moment)

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∂tρ− div

(ρ∇( δL

δρ ))

= 0 (x , t) ∈ Rn × (0,+∞),

ρ(x , t) ≥ 0,∫

Otto, Jordan & Kinderlehrer and Otto [’97–’01]

showed that (PDE) can be interpreted as

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∂tρ− div

(ρ∇( δL

δρ ))

= 0 (x , t) ∈ Rn × (0,+∞),

ρ(x , t) ≥ 0,∫

Otto, Jordan & Kinderlehrer and Otto [’97–’01]

showed that (PDE) can be interpreted as

the gradient flow of L in P2(Rn)

w.r.t. the Wasserstein distance W2 on P2(Rn)

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Examples

Ex.1: The potential energy functional

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Examples

L1(ρ) :=

V (x)ρ(x) dx

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

{L1(x , ρ,∇ρ) = L1(x , ρ) = ρV (x),δL1δρ = ∂ρL1(x , ρ) = V (x),

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Examples

The potential energy functional The linear transport equation

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

Ex.2: The entropy functional

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

L2(ρ) :=

ρ(x) log(ρ(x))dx

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

L2(ρ) :=

ρ(x) log(ρ(x))dx ,

{L2(x , ρ,∇ρ) = ρ log(ρ),δL1δρ = ∂ρL1(ρ) = log(ρ) + 1,

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

L2(ρ) :=

∂tρ− div(ρ∇(log(ρ) + 1)) = 0

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

The entropy functional The heat equation

L2(ρ) :=

∂tρ−∆ρ = 0

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

Ex.3: The internal energy functional

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

L3(ρ) :=

m − 1ρm(x) dx , m 6= 1

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

L3(ρ) :=

m − 1ρm(x) dx ,

{L3(x , ρ,∇ρ) = 1

m−1ρm,δL3δρ = ∂ρL3(ρ) = m

m−1ρm−1,

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

L3(ρ) :=

m − 1ρm(x) dx ,

{L3(x , ρ,∇ρ) = 1

m−1ρm−1,

∂tρ− div(ρ∇ρm

ρ) = 0

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

The internal functional The porous media equation

L3(ρ) :=

m − 1ρm(x) dx ,

{L3(x , ρ,∇ρ) = 1

m−1ρm−1,

∂tρ−∆ρm = 0 Otto ’01

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

Ex.4: The (Entropy+ Potential) energy functional

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

L4(ρ) :=

(ρ(x) log(ρ(x)) + ρ(x)V (x)) dx ,

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

L4(ρ) :=

(ρ log(ρ)+ρV ),

{L4(x , ρ,∇ρ) = ρ log(ρ) + ρV (x),δL4δρ = ∂ρL4(x , ρ) = log(ρ) + 1 + V (x),

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

L4(ρ) :=

(ρ log(ρ)+ρV ),

∂tρ− div(ρ∇(log(ρ) + 1 + V )) = 0

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Examples

L1(ρ) :=

V (x)ρ(x) dx ,

∂tρ− div(ρ∇V ) = 0

Entropy+Potential The Fokker-Planck equation

L4(ρ) :=

(ρ log(ρ)+ρV ),

∂tρ−∆ρ− div(ρ∇V ) = 0 Jordan-Kinderlehrer-Otto ’97

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New insight

• This gradient flow approach has brought several developmentsin:

I approximation algorithms

I asymptotic behaviour of solutions (new contraction andenergy estimates)

I applications to functional inequalities.....

[Agueh, Brenier, Carlen, Carrillo, Dolbeault, Gangbo,

Ghoussoub,McCann, Otto, Vazquez, Villani..]

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Towards gradient flows in metric spaces

• In [Gradient flows in metric and in theWasserstein spaces Ambrosio, Gigli, Savare

’05], these existence, approximation, long-timebehaviour results were recovered for general

Gradient Flows in Metric Spaces

• Metric spaces are a suitable framework forrigorously interpreting diffusion PDE as gradient flowsin the Wasserstein spaces ( no linear structure!)

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• The approach of [AGS’05] based on the theory of MinimizingMovements & Curves of Maximal Slope [De Giorgi, Marino,

Tosques, Degiovanni, Ambrosio.. ’80∼’90]

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• [AGS’05]: ⇒ refined existence, approximation, long-timebehaviour for Curves of Maximal Slope & applications to gradientflows in Wasserstein spaces

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• [R.-Segatti-Stefanelli’06]: complement AGS’s results on thelong-time behaviour of Curves of Maximal Slope

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Gradient flows in metric spaces: heuristics

I A complete metric space (X , d),

I a proper functional φ : X → (−∞,+∞]

Problem:How to formulate the gradient flow equation

“u′(t) = −∇φ(u(t))”, t ∈ (0,T )

in absence of a natural linear or differentiable structure on X?

To get some insight, let us go back to the euclidean case...

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“u′(t) = −∇φ(u(t))”, t ∈ (0,T )

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“u′(t) = −∇φ(u(t))”, t ∈ (0,T )

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“u′(t) = −∇φ(u(t))”, t ∈ (0,T )

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Given a proper (differentiable) function φ : Rn → (−∞,+∞]

u′(t) = −∇φ(u(t)) ⇔ |u′(t) +∇φ(u(t))|2 = 0

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2〈u′(t),∇φ(u(t))〉

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2d

dtφ(u(t)) = 0

So we get the equivalent formulation:

This involves the modulus of derivatives, rather than derivatives,hence it can make sense in the setting of a metric space!

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u′(t) = −∇φ(u(t))

⇔ |u′(t) +∇φ(u(t))|2 = 0

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2〈u′(t),∇φ(u(t))〉

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2d

dtφ(u(t)) = 0

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u′(t) = −∇φ(u(t)) ⇔ |u′(t) +∇φ(u(t))|2 = 0

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2〈u′(t),∇φ(u(t))〉

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2d

dtφ(u(t)) = 0

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u′(t) = −∇φ(u(t)) ⇔ |u′(t) +∇φ(u(t))|2 = 0

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2〈u′(t),∇φ(u(t))〉

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2d

dtφ(u(t)) = 0

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u′(t) = −∇φ(u(t)) ⇔ |u′(t) +∇φ(u(t))|2 = 0

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2〈u′(t),∇φ(u(t))〉

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2d

dtφ(u(t)) = 0

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u′(t) = −∇φ(u(t)) ⇔ |u′(t) +∇φ(u(t))|2 = 0

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2〈u′(t),∇φ(u(t))〉

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2d

dtφ(u(t)) = 0

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u′(t) = −∇φ(u(t)) ⇔ |u′(t) +∇φ(u(t))|2 = 0

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2〈u′(t),∇φ(u(t))〉

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2d

dtφ(u(t)) = 0

dtφ(u(t)) = −1

2|u′(t)|2 − 1

2|∇φ(u(t))|2

This involves

the modulus of derivatives, rather than derivatives,hence it can make sense in the setting of a metric space!

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u′(t) = −∇φ(u(t)) ⇔ |u′(t) +∇φ(u(t))|2 = 0

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2〈u′(t),∇φ(u(t))〉

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2d

dtφ(u(t)) = 0

dtφ(u(t)) = −1

2|u′(t)|2 − 1

2|∇φ(u(t))|2

This involves the modulus of derivatives, rather than derivatives,

hence it can make sense in the setting of a metric space!

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u′(t) = −∇φ(u(t)) ⇔ |u′(t) +∇φ(u(t))|2 = 0

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2〈u′(t),∇φ(u(t))〉

⇔ |u′(t)|2 + |∇φ(u(t))|2 + 2d

dtφ(u(t)) = 0

dtφ(u(t)) = −1

2|u′(t)|2 − 1

2|∇φ(u(t))|2

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Metric derivatives and slopes

• Setting: A complete metric space (X , d)

“Surrogates” of (the modulus of) derivatives

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Given a curve u : (0,T ) → X ,

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Given a curve u : (0,T ) → X ,u′(t) |u′|(t) metric derivative:

|u′|(t) := limh→0

d(u(t), u(t + h)

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Given a curve u : (0,T ) → X ,u′(t) |u′|(t) metric derivative:

|u′|(t) := limh→0

d(u(t), u(t + h)

⇒ definition of the space of absolutely continuous curves(AC(0,T ;X ));

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Given a proper functional φ : X → (−∞,+∞],

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Given a proper functional φ : X → (−∞,+∞],−∇φ(u) |∂φ| (u) local slope:

|∂φ| (u) := lim supv→u

(φ(u)− φ(v))+

d(u, v)u ∈ D(φ)

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Given a proper functional φ : X → (−∞,+∞],−∇φ(u) |∂φ| (u) local slope:

|∂φ| (u) := lim supv→u

(φ(u)− φ(v))+

d(u, v)u ∈ D(φ)

The local slope in an upper gradient for φ, i.e. for any curvev ∈ AC(0,T ;X ) s.t. φ ◦ u is absolutely continuous on (0,T )∣∣∣∣ d

dtφ(u(t))

∣∣∣∣ ≤ |u′|(t) |∂φ| (u(t)) a.e. in (0,T ).

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Definition of Curve of Maximal Slope

(2-)Curve of Maximal Slope

We say that an absolutely continuous curve u : (0,T ) → X is a(2-)curve of maximal slope for φ if

dtφ(u(t)) = −1

2|u′|2(t)− 1

2|∂φ|2(u(t)) a.e. in (0,T ).

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Definition of Curve of Maximal Slope

(2-)Curve of Maximal Slope

We say that an absolutely continuous curve u : (0,T ) → X is a(2-)curve of maximal slope for φ if

dtφ(u(t)) = −1

2|u′|2(t)− 1

2|∂φ|2(u(t)) a.e. in (0,T ).

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Definition of p-Curve of Maximal SlopeConsider p, q ∈ (1,+∞) with 1

p + 1q = 1.

p-Curve of Maximal Slope

We say that an absolutely continuous curve u : (0,T ) → X is ap-curve of maximal slope for φ if

dtφ(u(t)) = −1

p|u′|p(t)− 1

q|∂φ|q(u(t)) a.e. in (0,T ).

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p + 1q = 1.

dtφ(u(t)) = −1

p|u′|p(t)− 1

q|∂φ|q(u(t)) a.e. in (0,T ).

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p + 1q = 1.

dtφ(u(t)) = −1

p|u′|p(t)− 1

q|∂φ|q(u(t)) a.e. in (0,T ).

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p + 1q = 1.

dtφ(u(t)) = −1

p|u′|p(t)− 1

q|∂φ|q(u(t)) a.e. in (0,T ).

2-curves of maximal slope in P2(Rn) lead (for a suitable φ) to thelinear transport equation

∂tρ− div(ρ∇V ) = 0

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p + 1q = 1.

dtφ(u(t)) = −1

p|u′|p(t)− 1

q|∂φ|q(u(t)) a.e. in (0,T ).

p-curves of maximal slope in Pp(Rn) lead (for a suitable φ) to the“doubly linear” transport equation

∂tρ−∇ · (ρjq (∇V )) = 0 jq(r) :=

{|r |q−2r r 6= 0,

0 r = 0.

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An existence result

I Given an initial datum u0 ∈ X , does there exist a p−curve ofmaximal slope u on (0,T ) fulfilling u(0) = u0?

I In [AGS’05] existence is proved by passing to the limit in anapproximation scheme by time discretization

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An existence result

I Given an initial datum u0 ∈ X , does there exist a p−curve ofmaximal slope u on (0,T ) fulfilling u(0) = u0?

I In [AGS’05] existence is proved by passing to the limit in anapproximation scheme by time discretization

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An existence result

Theorem [Ambrosio-Gigli-Savare ’05]

Suppose that

I φ is lower semicontinuous

I φ is coercive

I the map v 7→ |∂φ|(v) is lower semicontinuous

Then, for all u0 ∈ D(φ) there exists a p-curve of maximal slope ufor φ, fulfilling u(0) = u0.

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An existence result

Suppose that

I φ is coercive

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An existence result

Suppose that

I φ is coercive

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An existence result

Suppose that

I φ is coercive

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An existence result

Suppose that

I φ is coercive

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An existence result

Suppose that

I φ is coercive

A sufficient condition for |∂φ| to be lower semicontinuous is

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An existence result

Suppose that

I φ is coercive

φ is λ-geodesically convex, for λ ∈ R

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An existence result

Suppose that

I φ is coercive

∀v0, v1 ∈ D(φ) ∃ (constant speed) geodesic γ, γ(0) = v0, γ(1) = v1,

φ is λ-convex on γ.

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An existence result

Suppose that

I φ is coercive

∀v0, v1 ∈ D(φ) ∃ (constant speed) geodesic γ, γ(0) = v0, γ(1) = v1,

φ(γt) ≤ (1− t)φ(v0) + tφ(v1)−λ

2t(1− t)d2(v0, v1) ∀ t ∈ [0, 1].

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An existence result

Suppose that

I φ is coercive

φ is λ-geodesically convex, for λ ∈ R

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Uniqueness for 2-curves of maximal slope

• Uniqueness proved only for p = 2!

Main assumptions (simplified):

I φ is λ-geodesically convex, λ ∈ R,

I existence and uniqueness of the curve of maximal slope

I Generation of a λ-contracting semigroup

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I a “structural property” of the metric space (X , d)

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I (X , d) is the Wasserstein space (P2(Rd),W2)

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Long-time behaviour for 2-curves of maximal slope

Main assumptions:

I p = 2

I φ is λ-geodesically convex, λ ≥ 0,

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Main assumptions:

I p = 2

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Main assumptions:

I p = 2

• λ > 0:exponential convergence of the solution as t → +∞ to theunique minimum point u of φ:

d(u(t), u) ≤ e−λtd(u0, u) ∀ t ≥ 0

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Main assumptions:

I p = 2

• λ = 0 + φ has compact sublevels:convergence to (an) equilibrium as t → +∞

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Our aim

“Fill in the gaps” in the study of the long-time behaviour ofp-curves of maximal slope:

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Our aim

Study the general case:

I φ λ-geodesically convex, λ ∈ RI p general

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Our aim

Namely, we comprise the cases:

1. p = 2, λ < 0 uniqueness: YES

2. p 6= 2, λ ∈ R uniqueness: NO

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Our point of viewNot the study of the convergence to equilibrium as t → +∞ of asingle trajectory

But the study of the long-time behaviour of a family oftrajectories (starting from a bounded set of initial data):convergence to an invariant compact set (“attractor”)?

On the other hand, for p 6= 2 no uniqueness result, nosemigroup of solutions

⇒ Need for a theory of global attractors for (autonomous)dynamical systems without uniqueness

Various possibilities: [Sell ’73,’96], [Chepyzhov & Vishik ’02],[Melnik & Valero ’02], [Ball ’97,’04]

In [R., Segatti, Stefanelli, Global attractors for curves of maximal slope, in

preparation]: Ball’s theory of generalized semiflows

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preparation]: Ball’s theory of generalized semiflowsRiccarda Rossi

Generalized Semiflows: definitionPhase space: a metric space (X , dX )

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Generalized Semiflows: definitionA generalized semiflow S on X is a family of mapsg : [0,+∞) → X (“solutions”), s. t.

(Existence) ∀ g0 ∈ X ∃ at least one g ∈ S with g(0) = g0,

(Translation invariance) ∀ g ∈ S and τ ≥ 0, the mapg τ (·) := g(·+ τ) is in S,

(Concatenation) ∀ g , h ∈ S and t ≥ 0 with h(0) = g(t), thenz ∈ S, where

z(τ) :=

{g(τ) if 0 ≤ τ ≤ t,

h(τ − t) if t < τ,

(U.s.c. w.r.t. initial data) If {gn} ⊂ S and gn(0) → g0,∃ subsequence {gnk

} and g ∈ S s.t. g(0) = g0 andgnk

(t) → g(t) for all t ≥ 0.

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Generalized Semiflows: dynamical system notionsWithin this framework:

I orbit of a solution/set

I ω-limit of a solution/set

I invariance under the semiflow of a set

I attracting set (w.r.t. the Hausdorff semidistance of X )

DefinitionA set A ⊂ X is a global attractor for a generalizedsemiflow S if:

♣ A is compact

♣ A is invariant under the semiflow

♣ A attracts the bounded sets of X (w.r.t. theHausdorff semidistance of X )

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Generalized Semiflows: dynamical system notionsWithin this framework:

I orbit of a solution/set

I ω-limit of a solution/set

I invariance under the semiflow of a set

I attracting set (w.r.t. the Hausdorff semidistance of X )

DefinitionA set A ⊂ X is a global attractor for a generalizedsemiflow S if:

♣ A is compact

♣ A is invariant under the semiflow

♣ A attracts the bounded sets of X (w.r.t. theHausdorff semidistance of X )

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Long-time behaviour for p-curves of maximal slope

dtφ(u(t)) = −1

p|u′|p(t)− 1

q|∂φ|q(u(t)) for a.e. t ∈ (0,T ),

Choice of the phase space:

X = D(φ) ⊂ X ,

dX (u, v) := d(u, v) + |φ(u)− φ(v)| ∀ u, v ∈ X .

Choice of the solution notion: We consider the set S of thelocally absolutely continuous p-curves of maximal slope for φu : [0,+∞) → X .

I ¿ Is S a generalized semiflow?

I ¿ Does S possess a global attractor?

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dtφ(u(t)) = −1

p|u′|p(t)− 1

q|∂φ|q(u(t)) for a.e. t ∈ (0,T ),

X = D(φ) ⊂ X ,

dX (u, v) := d(u, v) + |φ(u)− φ(v)| ∀ u, v ∈ X .

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dtφ(u(t)) = −1

p|u′|p(t)− 1

q|∂φ|q(u(t)) for a.e. t ∈ (0,T ),

X = D(φ) ⊂ X ,

dX (u, v) := d(u, v) + |φ(u)− φ(v)| ∀ u, v ∈ X .

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dtφ(u(t)) = −1

p|u′|p(t)− 1

q|∂φ|q(u(t)) for a.e. t ∈ (0,+∞),

X = D(φ) ⊂ X ,

dX (u, v) := d(u, v) + |φ(u)− φ(v)| ∀ u, v ∈ X .

Riccarda Rossi

dtφ(u(t)) = −1

p|u′|p(t)− 1

q|∂φ|q(u(t)) for a.e. t ∈ (0,+∞),

X = D(φ) ⊂ X ,

dX (u, v) := d(u, v) + |φ(u)− φ(v)| ∀ u, v ∈ X .

Riccarda Rossi

Theorem 1 [R., Segatti, Stefanelli ’06]

Suppose that

I φ is coercive

(the same assumptions of the existence theorem in [A.G.S. ’05])Then,

S is a generalized semiflow.

Riccarda Rossi

Suppose that

I φ is coercive

Riccarda Rossi

Suppose that

I φ is coercive

(the same assumptions of the existence theorem in [A.G.S. ’05])

Then,S is a generalized semiflow.

Riccarda Rossi

Suppose that

I φ is coercive

Riccarda Rossi

Suppose that

I φ is coercive

I φ is continuous along sequences with bounded energies andslopes

I the set Z (S) the equilibrium points of S

Then,S admits a global attractor A.

Riccarda Rossi

Suppose that

I φ is coercive

Riccarda Rossi

Suppose that

I φ is coercive

Riccarda Rossi

Suppose that

I φ is coercive

Riccarda Rossi

Suppose that

I φ is coercive

Z (S) = {u ∈ D(φ) : |∂φ|(u) = 0}

Riccarda Rossi

Suppose that

I φ is coercive

is bounded in (X , dX ).

Riccarda Rossi

Suppose that

I φ is coercive

is bounded in (X , dX ).

Riccarda Rossi

The transport equation

∂tρ− div (ρjq(∇V )) = 0 in Rn × (0,+∞)

{p > 2,1p + 1

• The generalized semiflow S admits a global attractor

Riccarda Rossi

∂tρ− div (ρjq(∇V )) = 0 in Rn × (0,+∞)

{p > 2,1p + 1

• Consider the set S of solutions ρ arising from the gradient flowin (Pp(Rn),Wp) of the functional

V(ρ) :=

ρV dx

Riccarda Rossi

∂tρ− div (ρjq(∇V )) = 0 in Rn × (0,+∞)

{p > 2,1p + 1

Assumptions on V

I V : Rn → (−∞,+∞] proper and lower semicontinuous

I ∃ ε, M1,M2 > 0 : V (x) ≥ M1|x |p+ε −M2 for all x ∈ Rn

I V is λ-convex, λ ≤ 0

Riccarda Rossi

∂tρ− div (ρjq(∇V )) = 0 in Rn × (0,+∞)

{p > 2,1p + 1

Assumptions on V

I V : Rn → (−∞,+∞] proper and lower semicontinuous

Riccarda Rossi

The drift diffusion equation

∂tρ− div(

ρjq(∇(ρF ′(ρ)− F (ρ))

ρ+∇V )

)= 0 in Rn × (0,+∞)

Riccarda Rossi

∂tρ− div(

ρjq(∇(ρF ′(ρ)− F (ρ))

ρ+∇V )

)= 0 in Rn × (0,+∞)

• Consider the set S of solutions ρ arising from the gradient flowin (Pp(Rn),Wp) of the functional

φ(ρ) :=

F (ρ) + ρV dx

Riccarda Rossi

∂tρ− div(

ρjq(∇(ρF ′(ρ)− F (ρ))

ρ+∇V )

)= 0 in Rn × (0,+∞)

Assumptions on V

I V : Rn → (−∞,+∞] l.s.c.

Riccarda Rossi

∂tρ− div(

ρjq(∇(ρF ′(ρ)− F (ρ))

ρ+∇V )

)= 0 in Rn × (0,+∞)

Assumptions on F

I F : [0,+∞) → (−∞,+∞] l.s.c., F (0) = 0

I lim sups→+∞F (s)

s = +∞, lim infs↓0F (s)sα > −∞, α > n

I the map s 7→ F (e−s)es is convex and non increasing

I F satisfies the doubling condition

Riccarda Rossi

∂tρ− div(

ρjq(∇(ρF ′(ρ)− F (ρ))

ρ+∇V )

)= 0 in Rn × (0,+∞)

Assumptions on F

I F : [0,+∞) → (−∞,+∞] l.s.c., F (0) = 0

I lim sups→+∞F (s)

s = +∞, lim infs↓0F (s)sα > −∞, α > n

I the map s 7→ F (e−s)es is convex and non increasing

I F satisfies the doubling condition

Riccarda Rossi

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